{
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  "metadata": {
    "colab": {
      "name": "timeseries_prediction.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qvR6E_7o6jNZ",
        "colab_type": "text"
      },
      "source": [
        "# Online Learning and Prediction of Time-series with OR-ELM\n",
        "\n",
        "Online-Recurrent-Extreme-Learning-Machine (OR-ELM) for time-series prediction, implemented in python.\n",
        "\n",
        "[sorce code](https://github.com/chickenbestlover/Online-Recurrent-Extreme-Learning-Machine.git)\n",
        "\n",
        "\n",
        "## Requirements\n",
        "* Python 2.7\n",
        "* Numpy\n",
        "* Matplotlib\n",
        "* pandas\n",
        "* Expsuite (included in this repository)\n",
        "\n",
        "## Dataset\n",
        "* NYC taxi passenger count\n",
        " * Prediction of the New York City taxi passenger data. left.\n",
        "Example portion of taxi passenger data (aggregated at 30 min\n",
        "intervals).\n",
        "  * public data stream provided by the [New\n",
        "York City Transportation Authority](http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml )\n",
        "  * preprocessed (aggregated at 30 min intervals) by Cui, Yuwei, et al. in [\"A comparative study of HTM and other neural network models for online sequence learning with streaming data.\" Neural Networks (IJCNN), 2016 International Joint Conference on. IEEE, 2016.](http://ieeexplore.ieee.org/abstract/document/7727380/)\n",
        "  , [code](https://github.com/numenta/htmresearch/tree/master/projects/sequence_prediction)\n",
        "\n",
        "![example](https://raw.githubusercontent.com/chickenbestlover/Online-Recurrent-Extreme-Learning-Machine/master/fig/NYCexample.png)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-F02_hOBCUBY",
        "colab_type": "text"
      },
      "source": [
        "## Setup\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PnPqTgMeLHjJ",
        "colab_type": "text"
      },
      "source": [
        "### Clone OR-ELM Github repository"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zelDxdeHLXQv",
        "colab_type": "code",
        "outputId": "75efd5aa-99dc-402f-d46e-2df329b7497a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "source": [
        "!git clone https://github.com/chickenbestlover/Online-Recurrent-Extreme-Learning-Machine.git"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Online-Recurrent-Extreme-Learning-Machine'...\n",
            "remote: Enumerating objects: 3, done.\u001b[K\n",
            "remote: Counting objects:  33% (1/3)   \u001b[K\rremote: Counting objects:  66% (2/3)   \u001b[K\rremote: Counting objects: 100% (3/3)   \u001b[K\rremote: Counting objects: 100% (3/3), done.\u001b[K\n",
            "remote: Compressing objects: 100% (3/3), done.\u001b[K\n",
            "remote: Total 1091 (delta 0), reused 0 (delta 0), pack-reused 1088\u001b[K\n",
            "Receiving objects: 100% (1091/1091), 188.20 MiB | 22.27 MiB/s, done.\n",
            "Resolving deltas: 100% (315/315), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2h3ao1S-LoYn",
        "colab_type": "code",
        "outputId": "1192948d-a51b-4b6a-fd26-8790c7090a3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "import os\n",
        "print(os.getcwd())\n",
        "!ls\n",
        "os.chdir('Online-Recurrent-Extreme-Learning-Machine')\n",
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Online-Recurrent-Extreme-Learning-Machine\n",
            "algorithms\t\t\t\t   plotResults.py\n",
            "data\t\t\t\t\t   prediction\n",
            "errorMetrics.py\t\t\t\t   README.md\n",
            "expsuite\t\t\t\t   result\n",
            "fig\t\t\t\t\t   run.py\n",
            "Online-Recurrent-Extreme-Learning-Machine  timeseries_prediction.ipynb\n",
            "plot.py\n",
            "algorithms\t expsuite  plotResults.py  result\n",
            "data\t\t fig\t   prediction\t   run.py\n",
            "errorMetrics.py  plot.py   README.md\t   timeseries_prediction.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h_aj5KpDCgNr",
        "colab_type": "text"
      },
      "source": [
        "### Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0JAvYtyUCxxV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import csv\n",
        "#from optparse import OptionParser\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "from scipy import random\n",
        "import pandas as pd\n",
        "from numpy.linalg import pinv\n",
        "from numpy.linalg import inv\n",
        "random.seed(0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mv-uFTjAC_1i",
        "colab_type": "text"
      },
      "source": [
        "### Set hyper-parameters for the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2-vyg7EXDD0L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "numLags = 100\n",
        "predictionStep = 5\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gb9O9X0qDnPE",
        "colab_type": "text"
      },
      "source": [
        "### Read dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8JxQY9kDqkF",
        "colab_type": "code",
        "outputId": "cd544085-fff9-4fa7-9a4d-6e8439ef17e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dataSet = 'nyc_taxi'\n",
        "filePath = 'data/'+dataSet+'.csv'\n",
        "df = pd.read_csv(filePath, header=0, skiprows=[1,2], names=['time', 'data', 'timeofday', 'dayofweek'])\n",
        "df.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>data</th>\n",
              "      <th>timeofday</th>\n",
              "      <th>dayofweek</th>\n",
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              "  </thead>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  time   data  timeofday  dayofweek\n",
              "0  2014-07-01 00:00:00  10844          0          1\n",
              "1  2014-07-01 00:30:00   8127         30          1\n",
              "2  2014-07-01 01:00:00   6210         60          1\n",
              "3  2014-07-01 01:30:00   4656         90          1\n",
              "4  2014-07-01 02:00:00   3820        120          1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okwhajgYJANR",
        "colab_type": "code",
        "outputId": "53bb5504-3d02-4c6b-a0e5-d7bbe2e21928",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17520 entries, 0 to 17519\n",
            "Data columns (total 4 columns):\n",
            "time         17520 non-null object\n",
            "data         17520 non-null int64\n",
            "timeofday    17520 non-null int64\n",
            "dayofweek    17520 non-null int64\n",
            "dtypes: int64(3), object(1)\n",
            "memory usage: 547.6+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NvQsG8rtJDAQ",
        "colab_type": "code",
        "outputId": "27ac5d36-96a8-488e-c450-9f50cb4b6d0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "time         0\n",
              "data         0\n",
              "timeofday    0\n",
              "dayofweek    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMzKfBoDsRgB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "  # standardize data by subtracting mean and dividing by std\n",
        "  meanSeq = np.mean(df['data'])\n",
        "  stdSeq = np.std(df['data'])\n",
        "  df['data'] = (df['data'] - meanSeq)/stdSeq"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtRI834Zs-bM",
        "colab_type": "code",
        "outputId": "67d8a5b7-a26e-4ce7-ba15-143f514cf369",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>data</th>\n",
              "      <th>timeofday</th>\n",
              "      <th>dayofweek</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-07-01 02:00:00</td>\n",
              "      <td>-1.650131</td>\n",
              "      <td>120</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  time      data  timeofday  dayofweek\n",
              "0  2014-07-01 00:00:00 -0.620382          0          1\n",
              "1  2014-07-01 00:30:00 -1.018706         30          1\n",
              "2  2014-07-01 01:00:00 -1.299746         60          1\n",
              "3  2014-07-01 01:30:00 -1.527569         90          1\n",
              "4  2014-07-01 02:00:00 -1.650131        120          1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fEUPsiRyzuwA",
        "colab_type": "text"
      },
      "source": [
        "### Prepare input-target pairs "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MywlEDYUz1GG",
        "colab_type": "code",
        "outputId": "11ef9935-8d18-46f1-d021-3b64efa8e903",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "def getTimeEmbeddedMatrix(sequence, numLags=100, predictionStep=1):\n",
        "  print(\"generate time embedded matrix\")\n",
        "  inDim = numLags\n",
        "  X = np.zeros(shape=(len(sequence), inDim))\n",
        "  T = np.zeros(shape=(len(sequence), 1))\n",
        "  for i in xrange(numLags-1, len(sequence)-predictionStep):\n",
        "    X[i, :] = np.array(sequence['data'][(i-numLags+1):(i+1)])\n",
        "    T[i, :] = sequence['data'][i+predictionStep]\n",
        "  print('input shape: ',X.shape)\n",
        "  print('target shape: ',T.shape)\n",
        "  return (X, T)\n",
        "\n",
        "(X, T) = getTimeEmbeddedMatrix(df, numLags, predictionStep)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "generate time embedded matrix\n",
            "('input shape: ', (17520, 100))\n",
            "('target shape: ', (17520, 1))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oy9XZuCPz9qE",
        "colab_type": "code",
        "outputId": "3b7ebafd-a0bd-43cc-a231-bec0617713b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(X.shape, T.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "((17520, 100), (17520, 1))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bASwilvb3iTn",
        "colab_type": "text"
      },
      "source": [
        "# Online Sequential Extreme Learning Machine (OS-ELM)\n",
        "\n",
        "## Base operation: linear layer with nonlinear activation using NumPy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsOBcp4a2NRy",
        "colab_type": "code",
        "outputId": "a82d62f9-bdf3-4650-edc0-b5c23f3386b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "weights = np.random.random((20,100))\n",
        "print('weights:',weights.shape)\n",
        "\n",
        "bias = np.random.random((1,20)) * 2 -1\n",
        "print('bias:', bias.shape)\n",
        "\n",
        "features = np.random.random((1,100))\n",
        "print('input features:', features.shape)\n",
        "\n",
        "def linear(features,weights,bias):\n",
        "   return np.dot(features, np.transpose(weights)) + bias\n",
        "\n",
        "hidden = linear(features,weights,bias)\n",
        "print('hidden (before nonlinear activation):', hidden.shape)\n",
        "\n",
        "def sigmoidActFunc(features):\n",
        "  return 1.0 / (1.0 + np.exp(-features))\n",
        "\n",
        "hidden = sigmoidActFunc(hidden)\n",
        "print('hidden (after nonlinear activateion):', hidden.shape)\n",
        "\n",
        "def reluActFunc(features):\n",
        "  return np.maximum(0,features)\n",
        "hidden = reluActFunc(hidden)\n",
        "print('hidden (after nonlinear activateion):', hidden.shape)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('weights:', (20, 100))\n",
            "('bias:', (1, 20))\n",
            "('input features:', (1, 100))\n",
            "('hidden (before nonlinear activation):', (1, 20))\n",
            "('hidden (after nonlinear activateion):', (1, 20))\n",
            "('hidden (after nonlinear activateion):', (1, 20))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bM97dbNhNNQt",
        "colab_type": "text"
      },
      "source": [
        "##Set model hyper-parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SjDbZ7xNNT4x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "nDimInput = 100\n",
        "nDimOutput = 1\n",
        "numNeurons = 25\n",
        "lamb=0.0001\n",
        "outputWeightFF = 0.92 "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfAaruzuD0Nx",
        "colab_type": "text"
      },
      "source": [
        "## OSELM implementation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u8SdfxFK_c3-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class OSELM(object):\n",
        "  def __init__(self, inputs, outputs, numHiddenNeurons, forgettingFactor=0.999):\n",
        "    self.name = 'OSELM'\n",
        "    self.inputs = inputs\n",
        "    self.outputs = outputs\n",
        "    self.numHiddenNeurons = numHiddenNeurons\n",
        "\n",
        "    # input to hidden weights\n",
        "    self.inputWeights = None\n",
        "    # bias of hidden units\n",
        "    self.bias = None\n",
        "    # hidden to output layer connection\n",
        "    self.beta = None\n",
        "    # auxiliary matrix used for sequential learning\n",
        "    self.M = None\n",
        "\n",
        "    self.forgettingFactor = forgettingFactor\n",
        "\n",
        "  def calculateHiddenLayerActivation(self, features):\n",
        "    \"\"\"\n",
        "    Calculate activation level of the hidden layer\n",
        "    :param features feature matrix with dimension (numSamples, numInputs)\n",
        "    :return: activation level (numSamples, numHiddenNeurons)\n",
        "    \"\"\"\n",
        "    V = linear(features, self.inputWeights,self.bias)\n",
        "    #H = sigmoidActFunc(V)\n",
        "    H = reluActFunc(V)\n",
        "    return H\n",
        "\n",
        "\n",
        "  def initializePhase(self, lamb=0.0001):\n",
        "    \"\"\"\n",
        "    Step 1: Initialization phase\n",
        "    \"\"\"\n",
        "    # randomly initialize the input->hidden connections\n",
        "    self.inputWeights = np.random.random((self.numHiddenNeurons, self.inputs))\n",
        "    self.inputWeights = self.inputWeights * 2 - 1\n",
        "    self.bias = np.random.random((1, self.numHiddenNeurons)) * 2 - 1\n",
        "    # auxiliary matrix used for sequential learning\n",
        "    self.M = inv(lamb*np.eye(self.numHiddenNeurons))\n",
        "    # hidden to output layer connection\n",
        "    self.beta = np.zeros([self.numHiddenNeurons,self.outputs])\n",
        "\n",
        "\n",
        "\n",
        "  def train(self, features, targets):\n",
        "    \"\"\"\n",
        "    Step 2: Sequential learning phase\n",
        "    :param features feature matrix with dimension (numSamples, numInputs)\n",
        "    :param targets target matrix with dimension (numSamples, numOutputs)\n",
        "    \"\"\"\n",
        "    (numSamples, numOutputs) = targets.shape\n",
        "    assert features.shape[0] == targets.shape[0]\n",
        "\n",
        "    H = self.calculateHiddenLayerActivation(features)\n",
        "    Ht = np.transpose(H)\n",
        "\n",
        "\n",
        "    self.M = (1/self.forgettingFactor) * self.M - np.dot((1/self.forgettingFactor) * self.M,\n",
        "                                     np.dot(Ht, np.dot(\n",
        "                                       pinv(np.eye(numSamples) + np.dot(H, np.dot((1/self.forgettingFactor) * self.M, Ht))),\n",
        "                                       np.dot(H, (1/self.forgettingFactor) * self.M))))\n",
        "    self.beta = self.beta + np.dot(self.M, np.dot(Ht, targets - np.dot(H, self.beta)))\n",
        "\n",
        "  def predict(self, features):\n",
        "    \"\"\"\n",
        "    Make prediction with feature matrix\n",
        "    :param features: feature matrix with dimension (numSamples, numInputs)\n",
        "    :return: predictions with dimension (numSamples, numOutputs)\n",
        "    \"\"\"\n",
        "    H = self.calculateHiddenLayerActivation(features)\n",
        "    prediction = np.dot(H, self.beta)\n",
        "    return prediction\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PHkysgc9FyiD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net = OSELM(inputs=nDimInput,outputs=nDimOutput, numHiddenNeurons=numNeurons,forgettingFactor=outputWeightFF)\n",
        "net.initializePhase(lamb=lamb)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mqCLIUbKZXv",
        "colab_type": "text"
      },
      "source": [
        "### Online learning and prediction of OS-ELM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQxI2ZLxGaVm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "  predictions= []\n",
        "  target= []\n",
        "\n",
        "  for i in xrange(numLags, len(df)-predictionStep-1):\n",
        "    net.train(X[[i], :], T[[i], :])\n",
        "    Y = net.predict(X[[i+1], :])\n",
        "\n",
        "    predictions.append(Y[0][0])\n",
        "    target.append(T[i][0])\n",
        "\n",
        "    print \"{:5}th timeStep -  target: {:8.4f}   |    prediction: {:8.4f} \".format(i, target[-1], predictions[-1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "htj0fV2uHN2v",
        "colab_type": "text"
      },
      "source": [
        "  ### Evaluation: Calculate total Normalized Root Mean Square Error (NRMSE)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6m3aospaHP9v",
        "colab_type": "code",
        "outputId": "7740a39d-e5df-47b3-d03b-5a9e0ae0f4ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Reconstruct original value\n",
        "predictions = np.array(predictions)\n",
        "target = np.array(target)\n",
        "predictions = predictions * stdSeq + meanSeq\n",
        "target = target * stdSeq + meanSeq\n",
        "  \n",
        "def computeSquareDeviation(predictions, truth):\n",
        "  squareDeviation = np.square(predictions-truth)\n",
        "  return squareDeviation\n",
        "\n",
        "# Calculate NRMSE from skip_eval to the end\n",
        "skip_eval=100\n",
        "squareDeviation = computeSquareDeviation(predictions, target)\n",
        "squareDeviation[:skip_eval] = None\n",
        "nrmse = np.sqrt(np.nanmean(squareDeviation)) / np.nanstd(predictions)\n",
        "print(\"NRMSE {}\".format(nrmse))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NRMSE 0.322358641476\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BhooA33yHxU2",
        "colab_type": "text"
      },
      "source": [
        "### Plot predictions and target values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayHmnv4HH0AE",
        "colab_type": "code",
        "outputId": "0a41a666-28ad-49ee-e164-e620913993fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "source": [
        "  algorithm= net.name\n",
        "  plt.figure(figsize=(15,6))\n",
        "  targetPlot,=plt.plot(target,label='target',color='red',marker='.',linestyle='-')\n",
        "  predictedPlot,=plt.plot(predictions,label='predicted',color='blue',marker='.',linestyle=':')\n",
        "  plt.xlim([5500,6500])\n",
        "  #plt.ylim([0, 30000])\n",
        "  plt.ylabel('value',fontsize=15)\n",
        "  plt.xlabel('time',fontsize=15)\n",
        "  plt.ion()\n",
        "  plt.grid()\n",
        "  plt.legend(handles=[targetPlot, predictedPlot])\n",
        "  plt.title('Time-series Prediction of '+algorithm+' on '+dataSet+' dataset',fontsize=20,fontweight=40)\n",
        "  plot_path = './predictionPlot.png'\n",
        "  plt.savefig(plot_path,plot_pathbbox_inches='tight')\n",
        "  plt.draw()\n",
        "  plt.show()\n",
        "  plt.pause(0)\n",
        "  print('Prediction plot is saved to'+plot_path)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IiIhIVijAFBERERERiZiNN94YgHnz5nHsscfWmHfUqFGsWLGiTvOPx+MMHjy4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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Prediction plot is saved to./predictionPlot.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mSGjR0FvSB8t",
        "colab_type": "text"
      },
      "source": [
        "## Plot prediction errors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mSiWUUEaIlQj",
        "colab_type": "code",
        "outputId": "a79bcfb5-f60e-4541-8e27-a515f0c1936f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "source": [
        "  algorithm= net.name\n",
        "  plt.figure(figsize=(15,6))\n",
        "  targetPlot,=plt.plot(np.abs(target-predictions),label='target',color='red',marker='.',linestyle='-')\n",
        "  plt.xlim([5500,6500])\n",
        "  #plt.ylim([0, 30000])\n",
        "  plt.ylabel('absolute error',fontsize=15)\n",
        "  plt.xlabel('time',fontsize=15)\n",
        "  plt.ion()\n",
        "  plt.grid()\n",
        "  plt.legend(handles=[targetPlot, predictedPlot])\n",
        "  plt.title('Prediction error of '+algorithm+' on '+dataSet+' dataset',fontsize=20,fontweight=40)\n",
        "  plot_path = './predictionErrorPlot.png'\n",
        "  plt.savefig(plot_path,plot_pathbbox_inches='tight')\n",
        "  plt.draw()\n",
        "  plt.show()\n",
        "  plt.pause(0)\n",
        "  print('Prediction error plot is saved to'+plot_path)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ADTfckFtvvZVDDjmE6dOnc+aZZ3LLLbdw2mmncfTRRwNw7LHH8uEPf5g77riD\nTTfdtOnxZ8yYwZw5c5g1axa3334773nPe/joRz/KZpttxrXXXsu1117L448/zkknncRVV13Frbfe\nypQpUzj99NNZsGABRx11FD/72c+45ZZb+EtfnlgfrDEgR5UkSZKkIfLMM7BoESxeDAsXlsf9YYst\ntmCPPfYA4L3vfS9nnHEGAO9617sAmD9/Ptdffz0HH3zwkn1efPFFAK677jouueQSAN73vvfx6U9/\nernjX3XVVXzoQx9ijTVKmLbBBhssV+aGG27g7rvvXlKPhQsXMm3aNO69914mTZrElltuuaR+M2bM\n6Jfn3cgAUpIkSdIqpbMTnntuEQCjRpXHXV7xCrjwwtLzuHAhjB5dHk+bVvI32mjZ8n/zN/XPGxFN\nH6+99toALF68mHHjxjFr1qxa+6+IzGSfffbhe9/73jLprc7Z3xzCKkmSJGm1Mm0aXH01nHhiue0K\nHlfWQw89xMzqosqLLrqI173udcvkr7vuukyaNIkf/vCHQAn2brvtNgD22GMPLr74YgAuvPDCpsff\nZ599+OY3v8miRSU4fvLJJwEYO3Yszz33HABTp07luuuuY/bs2QA8//zz/OEPf2Drrbdmzpw53H//\n/QDLBZj9xQBSkiRJ0mpn2jQ47rj+Cx4BXvOa13DWWWexzTbb8NRTT/HhD394uTIXXngh55xzDjvt\ntBPbbbcdl156KQBf+9rXOOuss9hhhx2YN29e0+N/8IMf5JWvfCU77rgjO+20ExdddBEA06dPZ7/9\n9mPPPfdk44035rzzzuPQQw9lxx13XDJ8dcyYMcyYMYO3vvWtTJ48mU022aT/nniDyMwBOfCqZMqU\nKXnzzTcPdTVWC52dnXR0dAx1NaSmbJ9qV7ZNtTPbp9rBPffcwzbbbLNM2nPPPcfYsWMHrQ5z5sxh\n//3358477xy0cw6UZq9nRNySmVN629ceSEmSJElSLQaQkiRJktSLiRMnrha9jyvLAFKSJEmSVMug\nBpARMSYiboqI2yLiroj49yr9vIh4ICJmVdvOVXpExBkRMTsibo+IyQ3HOjwi7qu2wxvSd42IO6p9\nzoj+mCtXkiRJ0pBy7pb+sbKv42D3QL4I7JWZOwE7A/tFxNQq71OZuXO1dS1i8mZgy2qbDnwdICI2\nAL4IvBbYHfhiRKxf7fN14KiG/fYb+KclSZIkaaCMGTOGJ554wiByJWUmTzzxBGPGjFnhY6zRj/Xp\nVZa/+Pzq4ahq66kVHABcUO13Q0SMi4hNgQ7gysx8EiAirqQEo53Aupl5Q5V+AXAg8PMBeDqSJEmS\nBsGECROYO3cujz322JK0BQuX+vllAAAgAElEQVQWrFQgNFyNGTOGCRMmrPD+gxpAAkTESOAW4NXA\nWZl5Y0R8GDg5Ir4AXA18JjNfBDYHHm7YfW6V1lP63CbpkiRJklZRo0aNYtKkScukdXZ2sssuuwxR\njYavQQ8gM/NlYOeIGAf8JCK2B44D/gKMBmYAnwa+NJD1iIjplGGxjB8/ns7OzoE83bAxf/58X0u1\nLdun2pVtU+3M9ql2ZdscGoMeQHbJzKcj4lpgv8w8rUp+MSK+A3yyejwP2KJhtwlV2jzKMNbG9M4q\nfUKT8s3OP4MSrDJlypR0gdz+4WLDame2T7Ur26bame1T7cq2OTQGexbWjaueRyJiLWAf4N7qukaq\nGVMPBLoWWLkMOKyajXUq8Exm/hm4Atg3ItavJs/ZF7iiyns2IqZWxzoMuHQwn6MkSZIkra4Guwdy\nU+D86jrIEcAPMvN/I+KaiNgYCGAW8KGq/OXAW4DZwAvA+wEy88mIOBH4bVXuS10T6gBHA+cBa1Em\nz3ECHUmSJEnqB4M9C+vtwHJXumbmXi3KJ/CRFnnnAuc2Sb8Z2H7laipJkiRJ6m6w14GUJEmSJK2i\nDCAlSZIkSbUYQEqSJEmSajGAlCRJkiTVYgApSZIkSarFAFKSJEmSVIsBpCRJkiSpFgNISZIkSVIt\nBpCSJEmSpFoMICVJkiRJtRhASpIkSZJqMYCUJEmSJNViAClJkiRJqsUAUpIkSZJUiwGkJEmSJKkW\nA0hJkiRJUi0GkJIkSZKkWgwgJUmSJEm1GEBKkiRJkmoxgJQkSZIk1WIAKUmSJEmqxQBSkiRJklSL\nAaQkSZIkqRYDSEmSJElSLQaQkiRJkqRaDCAlSZIkSbUYQEqSJEmSajGAlCRJkiTVYgApSZIkSarF\nAFKSJEmSVMugBpARMSYiboqI2yLiroj49yp9UkTcGBGzI+L7ETG6Sl+zejy7yp/YcKzjqvTfR8Sb\nGtL3q9JmR8RnBvP5SZIkSdLqbLB7IF8E9srMnYCdgf0iYipwKvCVzHw18BRwZFX+SOCpKv0rVTki\nYlvgEGA7YD/g7IgYGREjgbOANwPbAodWZSVJkiRJK2lQA8gs5lcPR1VbAnsBP6rSzwcOrO4fUD2m\nyt87IqJKvzgzX8zMB4DZwO7VNjsz/5iZC4GLq7KSJEmSpJW0xmCfsOolvAV4NaW38H7g6cxcVBWZ\nC2xe3d8ceBggMxdFxDPAhlX6DQ2Hbdzn4W7pr21Rj+nAdIDx48fT2dm5Us9Lxfz5830t1bZsn2pX\ntk21M9un2pVtc2gMegCZmS8DO0fEOOAnwNaDXYeqHjOAGQBTpkzJjo6OoajGaqezsxNfS7Ur26fa\nlW1T7cz2qXZl2xwaQzYLa2Y+DVwLTAPGRURXMDsBmFfdnwdsAVDlrwc80ZjebZ9W6ZIkSZKklTTY\ns7BuXPU8EhFrAfsA91ACyYOqYocDl1b3L6seU+Vfk5lZpR9SzdI6CdgSuAn4LbBlNavraMpEO5cN\n/DOTJEmSpNXfYA9h3RQ4v7oOcgTwg8z834i4G7g4Ik4CfgecU5U/B/huRMwGnqQEhGTmXRHxA+Bu\nYBHwkWpoLBFxDHAFMBI4NzPvGrynJ0mSJEmrr0ENIDPzdmCXJul/pMyg2j19AXBwi2OdDJzcJP1y\n4PKVrqwkSZIkaRlDdg2kJEmSJGnVYgApSZIkSarFAFKSJEmSVIsBpCRJkiSpFgNISZIkSVItBpCS\nJEmSpFoMICVJkiRJtRhASpIkSZJqMYCUJEmSJNViAClJkiRJqsUAUpIkSZJUiwGkJEmSJKkWA0hJ\nkiRJUi0GkJIkSZKkWgwgJUmSJEm1GEBKkiRJkmoxgJQkSZIk1WIAKUmSJEmqxQBSkiRJklSLAaQk\nSZIkqRYDSEmSJElSLQaQkiRJkqRaDCAlSZIkSbUYQEqSJEmSajGAlCRJkiTVYgApSZIkSarFAFKS\nJEmSVEuvAWREjImIP0TEfoNRIUmSJElSe+o1gMzMBcA4YPHAV0eSJEmS1K7qDmG9EHj/QFZEkiRJ\nktTe6gaQDwFviIjfRsSXIuIjEXF0w/bhOgeJiC0i4tqIuDsi7oqIY6v0EyJiXkTMqra3NOxzXETM\njojfR8SbGtL3q9JmR8RnGtInRcSNVfr3I2J0zecoSZIkSerBGjXL/Vd1uymwa5P8BL5e4ziLgH/N\nzFsjYixwS0RcWeV9JTNPaywcEdsChwDbAZsBV0XEVlX2WcA+wFzgtxFxWWbeDZxaHeviiPgGcGTN\nukmSJEmSelCrBzIzR/Syjax5nD9n5q3V/eeAe4DNe9jlAODizHwxMx8AZgO7V9vszPxjZi4ELgYO\niIgA9gJ+VO1/PnBgnbpJkiRJknpWtwey30XERGAX4EZgD+CYiDgMuJnSS/kUJbi8oWG3uSwNOB/u\nlv5aYEPg6cxc1KR89/NPB6YDjB8/ns7OzpV+ToL58+f7Wqpt2T7Vrmybame2T7Ur2+bQqB1ARsQ4\n4J+B1wEbAE8CvwZmZObTfTlpRKwDXAJ8LDOfjYivAydShsKeSBky+4G+HLOvMnMGMANgypQp2dHR\nMZCnGzY6OzvxtVS7sn2qXdk21c5sn2pXts2hUWsIa0T8HXAH8CVgbcqkOmtXj2+v8muJiFGU4PHC\nzPwxQGY+kpkvZ+Zi4FuUIaoA84AtGnafUKW1Sn8CGBcRa3RLlyRJkiStpLqzsH4FeBr428zcKzMP\nzcy9gL8DngJOr3OQ6hrFc4B7MvP0hvRNG4q9A7izun8ZcEhErBkRk4AtgZuA3wJbVjOujqZMtHNZ\nZiZwLXBQtf/hwKU1n6MkSZIkqQd1h7B2AIdn5jK9eZk5LyK+BHyn5nH2AN4H3BERs6q0zwKHRsTO\nlCGscyhDZcnMuyLiB8DdlBlcP5KZLwNExDHAFcBI4NzMvKs63qeBiyPiJOB3lIBVkiRJkrSS6gaQ\nSQnUmhlR5fd+kMzfANEk6/Ie9jkZOLlJ+uXN9svMP7J0CKwkSZIkqZ/UHcJ6LXBiRLyqMbF6/CXg\n6v6umCRJkiSpvdTtgfwYcA1wX0TcCjwCbALsSllO4xMDUz1JkiRJUruo1QOZmXOArYGPAncBoyjX\nJR4DbFPlS5IkSZJWY732QEbEmpRZTW/KzG8A3xjwWkmSJEmS2k6vPZCZ+SLwbWCzga+OJEmSJKld\n1Z1E5w5gq4GsiCSpm5kz4ZRTyq0kSVIbqDuJzseB8yLiz8AvMnPRANZJkjRzJnR0wKJFsOaacPXV\nMG3aUNdKkiQNc3V7IH9KGcJ6KbAgIh6LiEcbt4GroiQNQ52dsHAhLF5cbjs7h7pGkiRJtXsgzwJy\nICsiSWrQ0QEjRpQAcvTo8liSJGmI1ZmFdQTwLeCZzJw/8FWSJDFtGmy7LTz3HHzvew5flSRJbaHO\nENYRwBzgdQNbFUnSMtZeGzbf3OBRkiS1jTrLeCwCHgReMfDVkSQtsXhx2SRJktpE3Ul0TgU+FxEb\nDWRlJEkNDCAlSVKbqTuJzr7ApsCDEXEL8AjLTqqTmfmu/q6cJA1rixdDxFDXQpIkaYm6AeRGwO+7\nPZYkDSQDSEmS1GZqBZCZuedAV0SS1I0BpCRJajN1r4FcIorNIqJu76UkaUUsXgzpErySJKl91A4g\nI+ItEXEjsAB4GNixSv9WRLx3gOonScOXk+hIkqQ2UyuAjIjDgMuAe4HpQOOYqj8AR/Z/1SRpmDOA\nlCRJbaZuD+TngP/MzMOB/+mWdxewbb/WSpJkAClJktpO3QDyVcCVLfIWAOv2T3UkSUsYQEqSpDZT\nN4B8GNilRd4UYHb/VEeStIQBpCRJajN1A8hzgC9Wk+WsVaVFROwN/BvwrYGonCQNawaQkiSpzdRd\niuNUYAvgfODlKu16YCTwzcw8YwDqJknDmwGkJElqM7UCyMxM4CMRcTqwN7AR8CRwTWb+YQDrJ0nD\nlwGkJElqM3V7IAHIzPuB+weoLpKkRgaQkiSpzdS9BlKSNNgyyyZJktQmDCAlqV3ZAylJktqMAaQk\ntSsDSEmS1GYGNYCMiC0i4tqIuDsi7oqIY6v0DSLiyoi4r7pdv0qPiDgjImZHxO0RMbnhWIdX5e+L\niMMb0neNiDuqfc6IiBjM5yhJ/cYAUpIktZk+B5BVULdZRPRpAp7KIuBfM3NbYCplZtdtgc8AV2fm\nlsDV1WOANwNbVtt04OtVHTYAvgi8Ftidskbl+tU+XweOathvvxWopyQNPQNISZLUZmoHkBHxloi4\nEVgAPATsWKXPiIj31jlGZv45M2+t7j8H3ANsDhxAWWOS6vbA6v4BwAVZ3ACMi4hNgTcBV2bmk5n5\nFHAlsF+Vt25m3lAtPXJBw7EkadViAClJktpMrQAyIg4DLgPupfQENu53H3BkX08cEROBXYAbgfGZ\n+ecq6y/A+Or+5sDDDbvNrdJ6Sp/bJF2SVj0GkJIkqc3UHYb6OeA/M/O4iBgJfKch7y7gk305aUSs\nA1wCfCwzn228TDEzMyIGfN76iJhOCYYZP348nZ2dA33KYWH+/Pm+lmpbq1r7fP2iRWQmv1mF6qwV\ns6q1TQ0vtk+1K9vm0KgbQL6KMky0mQXAunVPGBGjKMHjhZn54yr5kYjYNDP/XA1DfbRKnwds0bD7\nhCptHtDRLb2zSp/QpPxyMnMGMANgypQp2dHR0ayY+qizsxNfS7WrVbJ9Rqx6dVafrZJtU8OG7VPt\nyrY5NOpeA/kwZbhpM1OA2XUOUs2Ieg5wT2ae3pB1GdA1k+rhwKUN6YdVE/dMBZ6phrpeAewbEetX\nk+fsC1xR5T0bEVOrcx3WcCxJWrU4hFWSJLWZuj2Q51BmOn0E+GmVFhGxN/BvwJdqHmcP4H3AHREx\nq0r7LPBl4AcRcSTwIPDOKu9y4C2UAPUF4P0AmflkRJwI/LYq96XMfLK6fzRwHrAW8PNqk6RVz+LF\nkAM+ol+SJKm2ugHkqZShpOcDL1dp1wMjgW9m5hl1DpKZvwFarcu4d5PyCXykxbHOBc5tkn4zsH2d\n+khSW7MHUpIktZlaAWRXIBcRp1MCvY2AJ4FrMvMPA1g/SRq+DCAlSVKbqRVARsQbgFsz837g/m55\nawO7ZuavBqB+kjQ8dQ1dNYCUJEltpO4kOtcC27bI27rKlyT1l67A0QBSkiS1kboBZKvrFgHWoUxw\nI0nqL12BY6YT6UiSpLbRcghrNWy1oyHpgxGxX7diY4C3Anf0f9UkaRhr7Hm8/nr41a+gowOmTRuy\nKkmSJPV0DeRrgX+p7idwMLCoW5mFwL3Ap/q/apI0jDUGkK9/PUTAmmvC1VcbREqSpCHTcghrZv5n\nZm6cmRsDDwF7dj1u2DbPzL0z89bBq7IkDQONAWRmebxwIXR2DlmVJEmS6i7jMWmgKyJJatB98pwR\nI2D06DKMVZIkaYjUXcbj6N7KZObZK18dSRKwfAC5335w/PEOX5UkSUOqVgAJ/HcPeV3TAxpASlJ/\n6R5A7rmnwaMkSRpytZbxyMwR3TdgA+BQ4DZarxEpSVoR3QNI14OUJEltoG4P5HIy82ng+xGxHvBN\nll3yQ5K0MroHjC+/PDT1kCRJalCrB7IXDwBT+uE4kqQu9kBKkqQ2tFIBZERsCvwrJYiUJPWXzGUf\nG0BKkqQ2UHcW1sdYOllOl9HAWGAB8I/9XC9JGt7sgZQkSW2o7jWQZ7F8ALkAmAv8IjOf6NdaSdJw\n5zWQkiSpDdUKIDPzhAGuhySpkT2QkiSpDfXHJDqSpP5mAClJktpQyx7IiPgtyw9bbSkzd++XGkmS\nDCAlSVJb6mkI6130IYCUJPUjr4GUJEltqGUAmZlHDGI9JEmN7IGUJEltqO4srEtExIbABsCTzr4q\nSQPEAFKSJLWh2pPoRMS7IuIe4FHgXuDRiLgnIg4esNpJ0nBlAClJktpQrR7IiDgUuBD4OXAK8Agw\nHngXcHFEjMzMiweslpI03BhASpKkNlR3COvngBmZ+aFu6RdExDeA4wEDSEnqL06iI0mS2lDdIayv\nBi5pkXdJlS9J6i/2QEqSpDZUN4B8BJjSIm9KlS9J6i8GkJIkqQ3VHcL6HeCEiBgJ/IgSMG4CHEwZ\nvnrKwFRPkoYpA0hJktSG6gaQXwJGAZ8B/r0h/a/AaVW+JKm/eA2kJElqQ7UCyMxcDHwuIk4Dtgc2\nBf4M3JmZTw1g/SRpeLIHUpIktaHa60ACZOZTmfnrzPxBddun4DEizo2IRyPizoa0EyJiXkTMqra3\nNOQdFxGzI+L3EfGmhvT9qrTZEfGZhvRJEXFjlf79iBjdl/pJUtswgJQkSW2oVgAZEf8UEUc2PJ4U\nEddHxNMRcUlEjKt5vvOA/ZqkfyUzd662y6tzbAscAmxX7XN2RIysrsM8C3gzsC1waFUW4NTqWK8G\nngKO7H4iSVolGEBKkqQ2VLcH8nhg3YbHZwIbAV8GJgMn1zlIZv4KeLLmOQ8ALs7MFzPzAWA2sHu1\nzc7MP2bmQsr6kwdERAB7USb5ATgfOLDmuSSpvXgNpCRJakN1A8i/Be4AiIj1gH2Bj2fml4HPAW9b\nyXocExG3V0Nc16/SNgcebigzt0prlb4h8HRmLuqWLkmrHnsgJUlSG6o7CytAVrdvBF4GrqoezwU2\nXok6fB04sTr+icB/AR9YiePVEhHTgekA48ePp7Ozc6BPOSzMnz/f11Jta1Vqn+NuvZWdGx4/9sgj\n3LWK1F19tyq1TQ0/tk+1K9vm0KgbQN4GvCcibgA+CFybmS9Wea8EHl3RCmTmI133I+JbwP9WD+cB\nWzQUnVCl0SL9CWBcRKxR9UI2lm923hnADIApU6ZkR0fHij4FNejs7MTXUu2q7dvnzJnQ2QkdHbDj\njstkbbzhhu1dd62Utm+bGtZsn2pXts2hUTeA/CzwM+BwYD6wT0PegcCNK1qBiNg0M/9cPXwH0DVD\n62XARRFxOrAZsCVwExDAlhExiRIgHgK8OzMzIq4FDqJcF3k4cOmK1kuSBtXMmfD3f1/ur7UW/Md/\nLJvvNZCSJKkN1F0H8jcR8UpgK+D+zHy6IftcygQ3vYqI7wEdwEYRMRf4ItARETtThrDOAf65Oudd\nEfED4G5gEfCRzHy5Os4xwBXASODczLyrOsWngYsj4iTgd8A5deolSUOucQjOwoVw223L5nsNpCRJ\nagO1r4HMzOeAW5qkX96HYxzaJLllkJeZJ9NkhtfqnMudNzP/SJmlVZJWLY1DcEaPhh12WDbfAFKS\nJLWBurOwEhE7RMRFETE7Ip6vbi+MiB1731uS1KNp05bev/pqeM1rls03gJQkSW2gVgAZEQdSeh93\noayz+PnqdjJwc5UvSeoP06a5DqQkSWpLdYewnkqZkOadmdm1nAcRcRzwwyr/p/1fPUkaplwHUpIk\ntaG6Q1i3AL7dGDwCVI+/xbLLakiSVpYBpCRJakN1A8ibge1a5G0P3No/1ZEkAQaQkiSpLbUcwhoR\nr2h4+AnK8hijKENVHwU2oazb+EHKWoySpBW17AAPr4GUJEltqadrIOdT1mbsEsApwH90SwO4kbIm\noyRpRfQWQNoDKUmS2kBPAeQHWDaAlCQNlN4CRgNISZLUBloGkJl53iDWQ5KGNwNISZK0Cqg7iY4k\naSAZQEqSpFVA3XUgiYh3AUcBWwFjuudn5ib9WC9JGl56CxidREeSJLWBWj2QEfFu4HxgNjABuAz4\n32r/Z4H/HqgKStKwYA+kJElaBdQdwvop4ETgI9XjszPzA8Ak4HHghQGomyQNHwaQkiRpFVA3gNwS\nuC4zXwZeBtYFyMzngFOBYwamepI0TBhASpKkVUDdAPJZYM3q/jxgm4a8ADbsz0pJ0rDjNZCSJGkV\nUHcSnd8COwJXUK5//EJELAIWAl8AbhiY6knSMGEPpCRJWgXUDSBPAV5V3f9Cdf/rlB7M3wL/3P9V\nk6RhxABSkiStAmoFkJl5A1UvY2Y+DRwQEWsCa2bmswNYP0kaHgwgJUnSKqD2OpDdZeaLwIv9WBdJ\nGr68BlKSJK0C6k6iI0kaSPZASpKkVYABpCS1AwNISZK0CjCAlKR2YAApSZJWAQaQktQOugeImcs+\n9hpISZLUBgwgJakdNAaQmfZASpKktmQAKUntoDFAXLzYAFKSJLUlA0hJageNQ1YXLTKAlCRJbckA\nUpLaQWOA+NJLrgMpSZLakgGkJLWDxoDRHkhJktSmDCAlqR0YQEqSpFWAAaQktQMDSEmStAoY1AAy\nIs6NiEcj4s6GtA0i4sqIuK+6Xb9Kj4g4IyJmR8TtETG5YZ/Dq/L3RcThDem7RsQd1T5nREQM5vOT\npBXW7BrIESOa50uSJA2Rwe6BPA/Yr1vaZ4CrM3NL4OrqMcCbgS2rbTrwdSgBJ/BF4LXA7sAXu4LO\nqsxRDft1P5cktadmPZBrrLE0zUl0JElSGxjUADIzfwU82S35AOD86v75wIEN6RdkcQMwLiI2Bd4E\nXJmZT2bmU8CVwH5V3rqZeUNmJnBBw7Ekqb01CyBHjmyeL0mSNETW6L3IgBufmX+u7v8FGF/d3xx4\nuKHc3Cqtp/S5TdKbiojplJ5Nxo8fT2dn54o/Ay0xf/58X0u1rXZun2Pvvptdq/s3XXcdmz74IJtl\n0hBC0nntteDI/NVSO7dNyfapdmXbHBrtEEAukZkZEdl7yX451wxgBsCUKVOyo6NjME672uvs7MTX\nUu2qrdvnmmsuubv75Mlw++0wahQsWLAkveMNb1i2V1KrjbZumxr2bJ9qV7bNodEOs7A+Ug0/pbp9\ntEqfB2zRUG5CldZT+oQm6ZLU/hqHqJ56Ksybt+wkOuB1kJIkaci1QwB5GdA1k+rhwKUN6YdVs7FO\nBZ6phrpeAewbEetXk+fsC1xR5T0bEVOr2VcPaziWJLW3xgDyoovghz90KQ9JktR2BnUIa0R8D+gA\nNoqIuZTZVL8M/CAijgQeBN5ZFb8ceAswG3gBeD9AZj4ZEScCv63KfSkzuybmOZoy0+tawM+rTZLa\nX/fgMHOZ4atNy0iSJA2yQQ0gM/PQFll7NymbwEdaHOdc4Nwm6TcD269MHSVpSDQLDl96qfcykiRJ\ng6gdhrBKkuoEh14DKUmShpgBpCS1gzoBpD2QkiRpiBlASlI76B4crrMOjBvXcxlJkqRBZgApSe2g\ne3C4aBGsvfayaTfeOHj1kSRJasIAUpLaQfcAcsECePHFZdN+85vBq48kSVITBpCS1A6aDU8dOxbW\nWgtGVB/VU6cObp0kSZK6MYCUpHbQLIBcd124+mo44IDyePLkwa2TJElSNwaQktQOmgWQCxbAtGmw\n//6ty0ganmbOhFNOKbeSNIjWGOoKSJJoHhw+/3y5HTmy3LoOpCQoQeNee5UfmcaMgWuuKT82SdIg\nsAdSktpBVwA5evTStPXXL7dd10DaAykJoLNz6SRbL75YHkvSIDGAlKR20BUcfu1rS9M22KDcGkBK\natTRAWtUg8hGjiyPJWmQGEBKUjvoCg4bJ8rp+oJoACmp0bRp8C//Uu6//e0OX5U0qAwgJakdNBvC\n2nXto9dASupuu+3K7dixQ1sPScOOAaQktYNmAaQ9kJJa6fpceOGFoa2HpGHHAFKS2kFXcDhq1NI0\nA0hJrSxcWG67ZmuWpEFiAClJ7cAAUlJfdM3Cag+kpEFmAClJ7aArOBw5cuk1jwaQklrp6oE0gJQ0\nyAwgJakddAWHI0YsDRwbp+kHJ9GRtJRDWCUNEQNISWoHXQFkhD2QknpnAClpiBhASlI76KkH0gBS\nUnddAeQzzwxtPSQNOwaQktQODCAl9UXXJDpPPQXXXTe0dZE0rBhASlI7qBNAnn8+zJzZfP+ZM+GU\nU1rnS1q9PPjg0vv77ON7X9KgMYCUpHbQGEB2vwby3nvL7TnnwN57L/9FceZM6OiA449vni9p9fPQ\nQ0vvL1wInZ1DVhVJw4sBpCS1g8xy26wH8vbby+3ixc2/KHZ2lvRW+ZJWPxtvvPT+qFHlR6ThYOZM\n+MIX/KFMGkIGkJLUDnoawrrbbkvzRo9e/otiVz40z5e0+hk3bun9b34Tpk0buroMlq7RFieeCHvu\n+f+z9+VhUhTn/5/a2YNzBUEBQQQRFDWCoOIYRQQVb8nPxBgT8UiCGI0maoxnNFHRaIyaeIFXNBr9\net8HiKyiO15RPPBCBBNQiYiwgOw59fvjnZd6u6a6p2d2dnfA+jzPPN3T09NdXf3WW+9dXon08Ogg\neAXSw8PDoxQQpUCOHk3bo48GZs/OFhT79DH7rt89PDw2PnARHQAYNqzj2tGeqKkBmppov6nJR1t4\neHQQvALp4eHhUQqIyoGsqqLtoYe6lcNFi2hbXe2VRw+P7wp4GQ8A+PbbjmtHe2LcOMMfEwkfbeHh\n0UHwCqSHh4dHKSDKA1lZSVspMEqwAtm3b9u1z8PDo7TQ2GiUqe+KAplMAkccQftTpniDmYdHB8Er\nkB4eHh6lgDgKpAxZk6itbdu2eXh4lB4aG00e5HdFgQSAIUNo6w1mHh7xUeSlvsqLchUPDw8Pj9Yh\nSoHkEFaXBzKVAh58kPYXLKDvHWmVT6UoL2ncOO8d8PBoSzQ2AptsAnz9desVyA1p3DI/rK/v2HZ4\neGwoSKWo6FRDA9C5c1FqJZSMB1IptVgp9a5Sap5S6o3MsU2VUrOUUgsy256Z40op9Tel1CdKqXeU\nUqPEdY7NnL9AKXVsRz2Ph4eHR14oNIS1pgZoaaF9rTu2qARXSDzvPL8epYdHW6OhoTgeyA1tHVku\norN6dce2w8NjQwEv9QUUbamvklEgM9hHaz1Sa71L5vvZAGZrrYcCmJ35DgAHAhia+UwBcCNACieA\nCwGMAbAbgAtZ6fTw8PAoabACqVR2EZ2oENZx40jp5P92ZFEJnqS09utReni0NYoVwrqhrSPLnsdv\nvunYdmzIKHI4o0eJY9w4I08Uac3YUlMgbRwO4I7M/h0AJonjd2rCKwB6KKX6AZgIYJbWeoXW+hsA\nswAc0KYt9IPQw8OjGGSJx1EAACAASURBVEinSQFUKr8Q1mQSGD6c9rt379jwMzkp+fUoPTzaFsVS\nIDe0cbtuHW1XruzYdmyoSKWAsWM3HI+zR+uRTALHHEP7//hHUeSEUsqB1ABmKqU0gOla6xkA+mit\nv8j8/iUAXuysP4D/iv8uyRwLO54FpdQUkPcSffr0QU0BFrfq+fMx4owzUNbYiHRlJd6+6irU7bBD\n3tfZmLBmzZqC+tLDoz1QyvQ5eNEiDFQKL9TUYMTq1egJ4KOFC/FFTQ2gNfZWCp99/DEWO9o/urER\n3QG0NDZibgc/37jM9q3LL8eqhobS92aUCEqZNm1Uz5+PHvPmYeXIkd/5Oa8jMWbVKtTV1WGzigos\n+fBDfNoK+hmX2b555ZWoc4zbUqLPbRctQj8AKxcvxrw2btPGSOsD77wTWzc3AwDSDQ1YfNtt+E9Y\ngbYNAKVEm6WMwfX12ArA699+i7VF6K9SUiD31FovVUptDmCWUupD+aPWWmeUy6Igo6DOAIBddtlF\njyvE4pZKrQ/XSjQ1YVRdXelb7toYNTU1KKgvPTzaASVNnzNnAokEtW+zzQAA2+6wA7bl9lZWYlC/\nfhjkan8mxDXR1FQyz7fzCScA3bp1dDM2GJQ0bUqkUsAZZwDNzUR3RSjG4FEgEgl03nJLoFs3DOzd\nGwOLQD+jTj7Zebyk6POWWwAAPdLptm1TKgX87neUOlBVtfHQ+ooVwO23AwDKqqqw9QknYOsN+LlK\nijZLGY89BgDYdaedgNGjW325kglh1VovzWz/B+BhUA7jskxoKjLb/2VOXwpgS/H3AZljYcfbBn5B\nWw8Pj2IhnTa5jHYOJEACTNg6kHy8pYUE+zDU1AAXXdQ+IUu+QuLGiSeeIIG6paX98+V8ykgQjY2k\nxHfp8t1axoN5y3/+07a0UFND4bIbSm5oXPTpY7alqhT7sV58sJc5TI7IEyWhQCqluiqluvM+gP0B\nvAfgMQBcSfVYAI9m9h8DMDlTjXV3AKsyoa7PAthfKdUzUzxn/8yxtkEyCRx5JO3/4helOQg9PDxa\nj/aYzKQCaedAAiQohjF+GX4UprilUsD48cAf/9g+eS8bcEiURwS6dzf77ZkvFzdvK5UCTjqJPhu7\n8NnQUBwFUhctuKt98EUms2n16rblZZK2i1R4pCTw+ee0XbeuNOXWGTOAvfby1byLDZYNijQ3l0oI\nax8ADyulAGrTv7TWzyilXgdwn1Lq5wA+A5DR1vAUgIMAfALgWwDHA4DWeoVS6mIAr2fO+5PWekWb\ntnybbWjbu3eb3qZo2JDWevLwKAXw+klNTfmFMcUZa/KcOApkGOOXimV9vTt0NJNLuf78mpq25QHe\nA7lx4u23aTtkCPDPf7bfPDJ7tvGuh9EvK5l83u23A3PmbLxzXWMj8aTWKpC8LAZA47ZTp9a3rS3x\n5Zdmvy15WTJJ/dvQANx888ZDR0szgXl1dcCqVbSWaKkglQJ+9SuzNBXn424sfd+RYPlhY1Igtdaf\nAhjhOP41gAmO4xqAM1Bfa30bgNuK3cZQMJGvWtVutywY7IHgSadUQxc8PEoJNTXB0I84k1mcsZZK\nkXWVBbbDDivcA9nYSNeorzcVCm20R6VF6cn4Lnggv2sGuVQKuO8+2l+0qH3vvfXWZj+MfmtqgiHc\n7WEo6UgUK4RV8pVVq0pfgeza1ey3tWewVy/y2G0oToI4YA8kAPzhD8BRR5XOGJHrGgOlmR42dy5w\n3XWkeB9/fOn0XS5sjCGsGzTWrKHtsmUd2444qKkhAXNji+f38GhL8OSlVHzFK85Y43O0Jsb+3/8W\nngPZ0GCsyGGePznJPfccbYsdlivvvbF6IDmcecaM714p/Joas15pOt2+cwivhdqzZ7jx0x6bG8KS\nFIUinTaFjFqrQEpjz4awNEZFhdm/++62FeB7ZpYSX9p25TTaHRxFAJAiVEr8S65rDFD7SklB44ik\n++4jr/Q++5RO3+VCkT2QXoFsLVavpu2GoEDKiVQpsqzFgU9mNqitpRwy3xffHSSTZJEfPjy+1z5O\n7oycKBMJYIstsj2QrEgCuUNYq6tpP47iVl9PQkOxlR8ZibExeiBTKWDvvYFzzwVOPpkE+O+SQW7v\nvc2+Uu2rnD2bKWewdi2w++7uc3bZhdoFAP36bdzhqxx2WmwFckOIpqqvN++Z04jaCmyYk167DRmp\nlDEgAqXHv5JJ4+1NJIBf/rJj22PD9pCWUt/lAssG3gNZImAFUsbklyrkRNrSAvzmN7kFx2efpWTm\nCy4oLStVR4AtTxddVLp9MWMGMHEibT2Kh+ZmYNCg+MKoPC8sTyyZpHcF0LIIffoUFsKqNQmT+SiQ\nzz1nPKTFXKuxrs7sb4wKZE2NEdzZEwcU39NVqkY7uQ5ejx7tp5ylUuuXHUBjIzBrlvu8JUtMGPUW\nW2y8yiNgxtfcuaQ8ftcUyMxSR+tlsLYCKwsbiwdSRhEwSslTn07TMiNVVdT3pUaP48YZ4wVQWn2X\nC94DWWLgENbFi0tvsrchiUbreJaTq66iQdwRJdtLDVJ4LMUF0q+/HjjxRFpP8MQTvRJZLPDSGIUy\n3SFDwn/j8Khtt81dRCcshJWP5QphlRgxom2WIJIK5MYYwmp7lhnFzCefO5c8faVotFuRqUnXrx/N\nfe1RvTOVIqOdtPo//bT73MWLadu7t5mbNxTkazR4+WXaPvMMeVqXLCmcVjZEBXLzzWm/rRVIVsxf\neKG0xmKhkCkZAM0FpVQPY/lymm9HZMqifPVVx7bHRjIJDB1qvj/zTOn0XS74HMgSA1ulOCSslBnM\n11+b/bj5XFtumd/5rUWpWt6B4LOXYmL3XXcFvz/4YMe0IwqpFHDsscDUqaX5jl1ordVOKlU2uOBH\nQ0PudSA5hNUeIzwZ5OOBHDIE+NnPaP9nPyveBLixeyBlP11wgfu4RCH87OqryVBVikY7nkOGDqU2\nrl3btvebOZMiYGTIHRAsqCPB5225ZesVyFSKCoy0B59KpWg+yWfZAlaitSbesXZt4TKIFCjvuaf0\nefO6dcYDGcVfiwE2mnz4YenLeHEwZgzNM3vvTWlMu+5aWgoQhwqXqgIJGJoAKLVlQ0AqReumAt4D\nWTL43//MfqlN9jakArn11vGsTv360Xb8+La3Usn8olJk1M3Nxmo3bFj73DMfAXTkyOD3I45omzYV\nilQK2HNP4M47genTSyf5PFcft3btJLbop1LAtGnB+7ACuXZtvGU8vv6aBE2Zu1iIArl6taFhua5f\na/Haa2Z/Y/RASshKkC7aKJSfbbopbdvLaJcPpAIJBAWptsC995IizSF3gwfTdu7c7P5MpYArrqD9\nd95pXTEYTle4+OL24VM1NTSO40YGpVImJ1SG0xUqg0j6ffjh0px/JdozhFUqqKUu48XBqlU0ng47\nDOjbt+3HcL6YPZu2PC+1hwKZj5y1bh15Sbfbjr63tQGjEDz/fPB5amup4Buvn/rxx0W5jVcgWwtZ\nLaqY5aTbwhPHk3/37iT8xFEGOWRzwoS2t1LJENFSY9SpFLD//kaQmT+/7SdZFkDjFjqRxQR+8ANg\nypS2a1shmDMnmHtRCu+Yl9KIsvzn64FkRZFRV2fWpzvvPOIRfB+m9zgKZFUV8M031G+y8AG3K1cI\nq1zrra7OLPchDUthzxOHF6VSwIUXmu/z50efv6FD5kTxxCwh+Vl9PRlO4oDf5+jRpRVaBmQrkFde\n2bY8sKoq+J3DwR94IHu8yuIW6TSFHhYaYssKHdA6PhV37OSzxE4qRV5ZFgKVMnyjUINDIektHQWt\nCwthLVSmastc544Aj+FevchY1V4KZJz+T6WAc86h/euvp+0dd7SOxzzySPR985WzHn+ctn370raU\nFEiO8LJlmvvuCy5vVCQFsiTWgdygwWW0GxtJQCjGZM/hLI2NQOfOxRMimHEMH06u7DhrmLEAVNYO\ntgabMcetEtsekAIFo63XGHMp1FH3+vRTKmzR0gIMHNg2bWoNdtst+L01k3Gx1t9jBYyX0nD1cT4e\nSB67klmvWkX5qHIRdOYVHGa3Zk08D6QUqLn/4uZAyiIbq1ebe0dVF2RPTEMDVaJ9/vnw/pb0CgDv\nvht+3Y5EsWhHKpCXXQYcd1zwelxsQWv63H47MHlyvDVEAfpPa9r3/PP0Ofjg4vEonkNYqL7hBuDW\nW9tO0bWF2+efp61Ucvi+XNW4pYXGTVOTodt8MW4cGYQbGwuvNsuCaVNT7nlcHn/66ei+tKtAak28\n9dVXgSefzO898Fjo1s0cU4pC6FlGaO17LfZaqU1N9Mz5eCB5Xd6mJuKb+dAr8/1OnTrGoFPs/lu+\nnLa9e5OM9emnrb9mLnD/19fT2Lz+ereBW67jynPJI49QnmEhfX/nnaRQAfTeXfP7M89kG/qiUhIm\nT6b9l16ibakokKkU8P3vG6OZ5JG27DVgQFFu6T2QYYhrrVq92oR5Dh9eHM9hsayfNngy3m47Cr0d\nPz53zoUcWG0NGUset0psa5DPu7LXJgKIEbalNXLXXc2+nNDD8O9/Uzn3Hj3cHpGOhiwms912hZfY\nL2ao87hxQWXN9T7z8UDOnm08hIy6uvBcMQ6zsz2QYTmQ8jtPqHFDWKUCWVdnFMh33gnvQ8mLchWO\nkn0JmHDDUgILMvnkmkmw1xagoiWMW27Jvl4yaazUAAlGd94ZzXPmzgUWLqT9N98snLZTKWC//YBL\nL839nKkU5SSfeGLu+7ECyUaHtl4CYNCg4HcWjlzhvckkhchvvjnw61/TsULzIJNJ4JRTaJ/Xn8wX\nhUbUbLtt9O+utS6//33a/9734reP+eh55wGnn26O77ADvdebbmo9f33hBXonxVwuiPlb9+6k1MUR\n4OfMof/lm1fc1GQUmnQ6v/nqsceyUxbyRSG5sbnQER7IO+4w7625mcaW61nGjTNzH8+FrfGI33+/\n2WfDrQ0OReV73X579HxoV+EuFQWypiY74oJ5pM0X2HvfSngF0gUOa8vF9JqbSaDgl/Hqq8VZWDrO\nGnKF4M03adu1KxE/L2IeNTjZupdvsYRCFOn77gt+b0vBhD0rcd9VMkkhZX37An/9Kx3LZ7IuBNID\n29xME3pYPk4qRflnn39OnpGPPmrbthUCmcuw446FW1OLGeqcTAInnUT7v/qVu035eCCl0s9YtYrC\nnyV23pm2YQpkWAirVAK5rXYI62OPuWnE9kAuXkz7X38dPgak4SSXwSSZBE44wXzfYovwc9sCcXiO\ny+OcDyQNSwUyTJGS+aVa0xiO4jlPPBE8/x//yK99DFmqPyp8lgXU6dPJS77XXtHVm997j4R2Lq5W\nVta2YX2sgJeX0706daLw2a22cnskKispv595c2vy49hYUGiBPFnuX+v4ETW5QsrZMDF0KCn+c+aY\n5VXmzo0/715zjfHmyYiJTp2C0RKt4a+PPkp0WExDA/PjTp3IaBbnHUtDQj70yjyzRw9qv+ynKDz0\nEHD44a2vpBwnNzZfWYs9kKxA5qK31iKVoigFiZYW97Mkk8Chh5Ih/Prrafy0JhdceurDYCtTzc3h\ndCqNpFyFu1QUSFf/MI+0jcq+iE4bgHOX7rwz3hpprFQxAaVSxVlYWk6Kf/1r8cJib7mF9u3BHKWk\ncgGQfNaYYqEkH0V6xgwjyDNaK5hEMdY5c0zly4YGKhOfq52ffELe5q5diam98Ubb5kHKOHUWBhsa\n3MKgtD5pDfz3v23TpjDEmcR44iora12BCymYxfUCZ9pX7crLYyGfPXg28vFASmsm4+OPjfeV283e\ndR5fcYvouNabZQ8h//bkk9QnJ50UfB+2AikVoDB+Jdeq/OUvc/MiDisD2reIThyj35w5pDRLy3a+\nYfJc4AEIhrCGCTktLUD//rTPYzhqfrCXfPnHPwrjL7byEmZVt0PzW1rCvQOpFIWT1dcTvwSAffdt\n27A+nmOffx645BK61+jRNC5c96yro/HMIZmtqcT64Ydmv5D5PJk09JVOk1fUHpMuxPEIaU3v+MYb\n6T5dutDxH/0o/rwbZhTmIk6AkQ1SKQy8++78aZHz8otZEIoV+9mzqX25FMhUinJ1GddcE59emWcy\nX4trSH/gAdq2VhYcO9bsu/qPDeH5ROOwwti7N73rdeuCkRXFhh1yDUTTQteutCbylClkDNpuu3g8\n5rnnsj2+8j+VlSb8VEKmcOSi02QSOPVU2mdDWzGKOOVrBEiliJecdBK147LL3EtzML3a79cv41Fk\ncGL6eecBN99sJt+ysnBiYqbAgj4LCDYRFuKN69yZtrxOXGshB7FtRfv738MHZyEKJAsl7OXMVTwi\nlSLvj7247b/+Vbhg8pe/RIfOSOE2naZy8ePHh7+jZ58lxjtvHnDyyUZZK3RyiEMTdun6KPBEwwUV\nVq2iylvtgenTqa9zhdmw92abbQpXIDkfhMMjTz89Xk5ZxqAx4owzstvHAluYJTYfD6RLYP300+zx\nU19PYT35eCArK7PHCGAmA85lYWu1HYIm2/D888FrRS1Lw5ZWFlKjUF9PvKusrH2X8aipoUkyTGDj\n0NUZM8xza51fmDxP2gw5CU+Y4BZy6uqMAikRZviw+X2UNTwKUnmJuo5rfgnzDsg5pLGRnmGnnQrn\n0XPnEs+I6v81a4j+9tqLimskk2TE++ILd4Gc1avJEFQMBVIaQwqJBNI6yOcaG4lX5hL04yiQ69YF\nxyPvc8XaOPPSmDFmn/lO587BfMjbb6d77bknBt96a/4GU1ZGhw8Pjo/WpPrwfx5+mIT/zz6LPl/m\n1QH5edwKVSDtvNtC6zlI3uHiL/mkGDDmzSM54f33DX3OmlVY++LAXv4MCCrGNurqjDF3wADq+xw8\npsebb1LIvu3xlbl+N9zgvo40BI4dS/2sdTh99ulD2wMOMO1tDf70p/zCvDn0/Kab6HPiiWRA4PZI\nHHIIne89kG0MOTm2tJjJKSrZlAcdn8uWDGkxYQtRvqEMXCJeeglaA9trIxFlQZECLhCP8UuGkSum\nHAiGWwGGyYSF4uVCbS3wu9+Z0BmbsaZSwOWXZ/8vigHPnElbXnOL+7IQqyozgCiGkUpRv9koK3Nb\n0Thka8wYaltLC3kHWpt/EadqGiv/ucIC2QM5dChVEy2kPdxvHH4ZB8KgoZqastvHAsXXX7ufOR8P\npD2WlCKBjC2ArIxxeCLznM8/jxfCKiHXkATc4bNSkJQKZCoFLFhg1tOzC8BI8AQrlywKw7p1JIRW\nVbWvB1KOQZdyZoeGMvIxANlFguT9dtkl3CM2dGg2zz3++Ghhht91a9ablUJsmPfiN7+hfQ4VA4gG\nXQKvDGeurKSwvkLzp1ihnzYt2nC3dm1wuRSAQqPXrnXPW6tXF88DKcOPZ8zIX1HmBdElwkIRJU3G\nVSDZyAxkG3fizEu9e5t9rj/Qo0fQWzFkCIVjptNQXP00bjVhwERFdOtm+m/u3NblRb74Im15zokq\nAgZk1y/IZzwVqkByuhBAPP7UUwubix9+2Oy76M+WtXIpqqkUGea1Jrn06qvp+I9/XLgyz56wsP+v\nWRPMXQZIdg579zyGATJAxJAVevLyUbbxRM55YWvHfv45pX/07WuWttpzz3CvLl+zVy+az1ujQL7w\nAlUuD5NVXQibh1zyCcs7XoFsY+y1V/A7E/zixeGEztY1Zk48aUmLyQ030MvKN3mbBctiKZDJJOVJ\nbL01cNRRwd/k2m02pAcybmiqtGwCua3odiGTqVNp/x//KGyC4fWxGLYQZg9AWbgkbHJhBa2sjIS7\nMWNosi0kfIvvH+WhlQaNsjKqolVdHV6JjYWpvn2NMt7a8vPjx0cbPlIpCmWzvWJhk9hXXxHDHTiw\nMA/kk0+afuN7Pv54bvoQ71S7PAmsQC5c6M6Llcn/L7+crWBKpVMKrJ07k/KwdCkVqgGCa3NKGnz7\nbeofpkUe9/PmmXPsYh4s6LEFeuedaSLcfntzjnxe2wuaTlMb+/cno0tYP7JnM07p7/p6UlyqqtrX\nAym9gRdemD1GJE3KdfPyKYRlFwkC6Fm7dXMLlg0N9Bk+nPJ52HhQXu42AgH03jt1IgFLKeDoowv3\n8HGfbL55bu9FWZn5PayAWTJJnn/2JvXrV7gCKb1CUXxq7dqgRwwwRetcaQfsvchXgXQZjuR/5RJJ\ncfHkk7S1K2K7lDsp4OXq05YWU9mVYSuQceYlKZjzPTfZJKhArloVVOC1phQYu9/DjI2sQMqibk89\n1bq8SF5CpqyMxsjq1dFzAMs+APV9PuOpEAUylSJ+LlHIc6ZSwO9/H/zuap9MXcmlqEq5QhYIKrR9\n48YZT1hYfYaHHjL7fO+onE7pgezZMxaPWStTROT4ku+rthb44x+z2/juu/SfLl3I6HPdddERZmvX\n0vyWSFA7W6NAsmOCEcdgmI8BhOc3H8LaxuBJiSEtVmGE/u23RHhnnEHfeTCycJxKAffcY86PK6yk\n0+Yas2fTdVwMOt8wkKYmYNSobOElaskHmaMlQ1OjGI49oHJZQ5NJssgDFLbJxTcKrb41apTZLysj\nhmCX15fvlwuaRFWmYgZ13HEmDyeRKEy4i+OhlR7jqirK29hxRwo7CbPaAcDIkUbRaI33IlfVOlYw\nbQYYFRY4fz4Jx2vXEn3nu0abzA/jvnnvvdxGBvGOPjrzTBMZwGOHJ6ilS01erHxmqQjZSnVtLfUx\nH3vjDXNunz6UN7t4sfF4/+QnRpGQiozWwLJldCyVMvkV0jKcS4Fkr9BOO5lzLr/cPL+tQCpFlt4v\nv6R+HDs2u4DKiy+a/nn11dy8hhXITp3aT4FMpUyeJkD9aEPmsw0caITQOCHQDFmZk98dl6V3CZY8\nJqurKZ/nhRfo/ey7b7S3d8AAMmgOGuS2NMcF8+GqqnDvBUd7VFYSfwGi+W46TbwvmWxdBUepjEd5\nTlweSL7ntdcGx77WxnvBHow4CmRYlJD0cOZSHOy5OJWi0DIgGCIHEC+/+WZaXoDPl+3M1ac87qMU\nyDg0vWIF/W+TTYyCZ3sg6+qyIw9sgzCn/7g8NqxAfvmlMfoxfyo0L5ILK/3wh3SN5ctzzwFSIYhT\nWIVRiAJ51VXZxwoJgbZDb3kJG4kHHwx+D6uRwJCyT0VFsCBMVPtSKYrqkn1s50+H8QyWLxMJuo+M\nYnDdU3oge/aM5YFcx6G+e+xB46umhtoq57zzzyejk6SVVIrmuK++onn63XeB//s/8x+XzC55UmUl\nGZULjfRi2RNwy6ouJJNGwWanAuCWp7gwoPdAtjEefZS2nTqRAiNjtMMUvxdfpDCQ8ePpO3sNWPmz\nk4ddYUsuJbCuzhDDe+/RvffcMzjBcTifvTB5FL7+mibqffYJHpdJ8xIyh2PZsmCIXBTjlwNeWr+j\nFN7qapoQ996b2schVXGWr7DBwiFACh+vN8TeIyBYIZInyM8/D5+IeILnQiLduhUeHrX77sHvLg9t\nMkkT+267mcIdr71GE7GrjSzsjBpFSl0iQRNsod4L2Yeud22HRXC4XFgYaypFlufVq8mo0tKSf2Vf\nZpqbb06CDiOXkUGMwV61tWTVlSEqckkChnxm+ZwsgPA9b701eEyGLvH6f4ARBMaONQLGfvuZc5Wi\nsVlWlm0l5mezQ1h5cuTJoKqKJjZenwoIhuHx+cOGUR92725CsrmNdgGVp54KPk8uY05HhLDaEQXy\nHQD0PLfdZr5/+SXxmIoK6qt8eAsLr3Ky1tpNy6zAMd0mk+TJkoYDG++/T+8+lSK+PHduYcJJY6Pp\n/zA+lUwS/XMkxXHHGeGO6dGGHV5WqAKZTJpIlSijk0uBXLSItq5wtXQ6GML6wAO5++/hh91RQmvW\nmGeN4lWpFI3rc881XhhJk7Zg9+tfk9HwzjvN+fL6zz0X3eY4CmQcrFhBAjrndAFuBTIzF6x/ClvZ\nsHNjJY9gxbSpCfjDH+i52BC4zTaFRfCwfDFkSPxaBGwIB0w0VRzjO7+XuApkKhUMO+Wxfscd+T+n\nbei2I7sAt2c8KmUomSRD0eDB1F8330zHL7ooeu3DceOoroSUNaUBCgiXCbk6+Fln0T25UvdTT0UX\nwgKIx3z7bU6FJ8E026sXeWG5HsOCBdknS1qR6VPpNNGrlNldESDMk1IpmkvmzSu8mCKHzAKkH7jW\nxrSxdm3QSeOqi8DgvvceyDbE2rXA2WfTPguDgwYZi7+0EjBqa4HXXyeC+3//j45x5UtWuuyJ45hj\ngt+fe44sd7bl07a4sNdPTnCyBDevb2Mzxdpa811rmjA23ZSYkpxwwsIJ58wxg+mDD4w1MZGg6rBs\n5bHB7R86lAh9zBjjsQoLf/3mG1PQIZmkPNL+/YkBz5iR3wCVg4sH0Ny5pkjShAnUF5zfKiuWhk1E\nPHGwYNKtGzG1fMqmM1yeIFtYa26mSe+AA6g/oiZpwCiQ3bsb78UbbxRuGePQybIy9yRvT25SgHd5\nFCSj5ufIN4x18WLaDh0aVKZyWbGF8LD5nDnAFVcElzjg3EwpDMvCUvbkJZcukN6+ysqgN79XL2Ph\n5b7q0sXwC6apIUOInnr2NEW7OERGPtu//x1sBxdJkh5IrYNh77KQEtPddtsRzTU20r2lENDcHAwN\ntJeqyZVjU2wPZBxBzxZkbEXbNuQ1NJjwrblz8+Mt3IfsRQaMkGPDViABMjjKpUAEqufPJwv4okX0\nTG+9RbypEOGE+UG3brQf5u1vaqKQ1GSSPkceScfTabdSVywFEggK9WF8d82abAXyoINoa3uwpMeX\nqy0//nju/pOVQiUvXrPGKFdRisPNNwfzka+4goye0tMj4fLaSCX/tdei29xaBZLH1MKF9A7lWqWb\nbBKkz1Wr1hdyWcVhoFz5lSF5rz2X8ZqmAN1zwgTglVfouyucdMYMiiaIWkqG5YuJE4MedNkOm2/U\n1Rm+8MILdG6cyqU8D9q1IMIg5zmlTGREISHQyaRZ3xMwYbgSTJ9yHsqVMlRWZpbR2mMPOhZV60Ma\nQ6RBM5kEfvYz3XhpPwAAIABJREFUc96jj7oVwi++oHtefLExWgGkaIdFU8kQViCnF3K9AvnZZ2aZ\npvr6YAoIQ9KKTE1KJExYNGPPPbP/zwpkTU3hxRSZPnksAOFV4G2wvLrttkTXLiUZoGfh9BzvgWxD\nrF5tJgCtqdPXrqWwVqXoJduM5s9/NufzhMCerLo6smxzqXMmSE5UZ9x5Jwk2tuXTHizSKszEbzOy\nL74IFmaZMYOYDzPJ556j+zBzl3klYYK8rMyltfne0gL89rfRyiBAHsv6eqqeJ5dGcQ22lSuDXqVB\ng4w3K99cCRl6xELKQw8Zj1BjIwl8HLIlERbKwRM89xsLNfmsJelqH0C0Zwtry5dTW3mCGDcumENl\nt1EqkKkUKVsff1yY8JlKBenbhWQy6MW1PTJ2LoYMyWWl6i9/ya9trEBqTRMZX+/ee6Otu2I8Zfl+\n5BpoUrCTBZxs5nvAAUap5mqwXbvSMRkGvdlmxsLbubMJ4dliCxJm+PrDhxM9Ll9uctFmz6YJV3rv\npWUbII+6bHdlZXZbn37a3Of992lbUUG8qr6eJiFeb4v7Y+ZMw0PYm8m/n3Zabu9IsTyQTz9NAk6u\n8ZVMApMm0fMPG0YTqk17Np5/Pr/Jnyf8BQvo2e64w/zGYdk2XArkZpsZg4WFHvPmmTZxrm/c9tlg\nvte/P9F3mMDwxRfBccz06+K5TU10nWIpkFIxDDMCuTyQEybQO/j+94PGLckDWTCLkwYhn1/yYqlA\nugwETBN2bvBjj9HY0ZpoQ3q/bYTN51Ftbo0CKSOXXnqJeBLz40SCriPzFevq1s99S9i4YKe8SN7b\n3ExVymfMoHtJBZKLhHB/2KG9115LYb8zZ9I2TIn85htq5957UxgwQHUPZIVXOS+//DI9B79nGXoZ\n1c+pFFXIBMx8mEuB3Htv2ipF7/7gg+l7oUs9SMOYK5KA57bLLzfyQa5w1NWrs+WYqOeS4eZ2WoyU\n2VxLWAHkeNh8c/MsPC9cd102X+fIJBnCCuTkM+sVSHvd3Q8/zFbMpHGY++yEE0gZ/vZbOp9pfObM\n7HmHeZKdAhA3RJk9uuedZ5YEAYIGtSg88wxtuY1ffhk05jO22ML0mz0XL1/uNsxmeFp3wGK6bngF\nEjCWB4AGSnMzWeHYwwcEGU0qFazqV16enZ80c6ax2rDwdeGFwRfGk7VtSWWhbcstaaLkRZt32MFM\nmDKctLycCIKFjsbGYGx8Y6PJVQtTIFMpqqR1/PGmjbbFS1Zty6UMAibs4y9/CU6irsEmPZDcTul2\nzyeHQHogeZ/brpRhGi4F8tRT3coIM29muNx/XDY9n+p0rqRrO+yT87hYgEkmTR9yOXsJKTxJK2gh\nwqf02ESFLVZWBi13EvZ9k0l6lpEjKS4fIEaej4L71lu0XbqU3geHf3D+Q5i3iukxKnTQhvRc2ML3\nzjtnC608AUtB4bPPjOC5Zo0JTXzlleCi1E8/bX7j85PJ4HuW1k4G068MYbVzuTkse8YMKnQAkIDL\n1+rWjcJmxo4165sCRM8nn0zLDkjkyrEppgeSFeY41enSaXr2hQup3yVdjRhBWxkuJEv/55r8ecI/\n/3zKj6mspIXsGfPnu9fndCmQjY1Evw6ar2MDI/MoFtoKyRPje7PQHBbG+sUXQZr5wQ9MG+z7Sh7D\n3+vrCy/UJQsSXXqpO6LFVURHKZoTBwwI8kHZ3/vskzvXisFWeh4TzMtXrw73QMqImldfDf7GBWLS\naRKwZai6Dc5rs5WlqDYzjyhEgZwxw8g1XGuBq5q2tGQLsatWrX/vDSyv2AZuO4SOQ+GlkUWeywVm\nVq4MFjWx16e28/sYUlZgGUVGSsg1nhsbyfCttRkL8tyouhQyB5G3uRRI9gROnEiyGqerFKpASlnB\ndW9WEPbbzyi5V10VbVCVodlxFMhk0oSdHnpo8NqSr4Q9o81j2BDskh/5eswzOb0qlweSFSTbOJdO\nZ8vm0iD12GO0/eEPaazyGOAUtvvuy5ZRWIGUtTvCwnFdYAMGG8IZURFZLNv8/vfAmWea6wBBR4NE\nv36Gt9khrO+8k22YZZ52wQUYCgxDDHgFEiCX8LBhNCHxy/nss6DVRDJ0O0zh+OOzraQjRgSrewJk\ncZEx5KzIjBkT9DT87nd0nIt6sLDzwQfm+jJv8cADg6Fq5eXGEsZtZ+uQS4HkkKmbbqLKp3vvTe3g\nsIa+fWmCcglJHHInwYOdmVI6HUwGd4VErlwZVCAbG4NWpzPOiD9Amen26WP2+f7DhwN/+xvtuxTI\nsJLgtgfSFmrCiuG4lBpmtBxeC2QryLYCCQCHH05bO0RPXrN796DFMF/hM5Wi8CupbHGYi41vviHP\nz8UXkxdLMmrXxNzURNdius/Hs5xKGRrnMBXO0/ziC2N1doUliZDqgAoWpVBKz4VtvZN0KavtjR8f\n9EZ89FHQwgjQ9WxlkJX1dDp8Ih83jpQyVtgBU5ZceiB5zNpFwB58MLhMEYNz7LbZhq4v+6S52bST\nr5drWR5WIBsa6Pm5SEGc5WDscvBSME2nSak/4QT3dZYuJZpzGU7Y0+EKAUskcudhXXttMI1AKQqD\n475Kp7PXlkulTIl8rmKbSlEkRFOT03BSz2P9iCOo7Vyc7Z578s+fYr7H9OAS7tatI74rPXDjxpFB\nRhorGXaUAwv8Bx6YXyTBE08Qz5CertNPd68j6/JAAtRmm1fL9iWTJBQmEtmpIzbsd8c0vmpVuALJ\nfEFWhLZRVkaKrl2tXaKxkfgGGwd79SLDaxRNujyQrjkBCM4/qRTwz38Gf+/UKcgP7II57IFMJNDI\nsoMt6HK/y+draTF8k8fJoEHZ7fvTn8w7t/tHVqyWkAokK0I1NeY57fx9TkFiOt9mG/M/V8VmhkzT\n4LktlwLJ8/ZRR9F1pbGlENTVBUOqbaxYQXJEeTnRO0De2Kjx6PJAvv8+ybFhy3Hw+TadxVUgZZj0\nxIkmXNyWT5hv2R7I6dMjn2m9B1IWS2N07hwcK1xFOJUyBtLDDiMZhsFhoq4IBsmTWNGUPDQX5PJK\nPJf36UP8Zu5c4JJLgs/Kss1551F4vGseZ14ql5fr1cvIKjISgGHLX8zTWlqgYuqGXoEETLGYkSNN\novLixaTB//CH9DIkQ+clPzhMYfLkYKgBQIOpd2+65v77m/twviJgiHTTTYOehrDke7nA81df0f0H\nDCCBWp67445BYX72bOOlcSmQH3wQDN/jOHcmvtGjadKS1akYe+2VzYBZYD/wQNraceUuhv3NN8Fw\nCDtkgSe2ODlRzMgGDDAWVbayVlUZz6+9RhcQzgjWrKHn4MFvK5B8Pclo7FAamY8BBEM+OD+AwV5o\nKSR160bvz7Vwsi08nXYafb/vvvjCZ20ttffmm4P0JD03jHXr6J2NHk2esilT6NmPPpp+Z0OMfFec\nPzVpEn2PW4UvlaJwcHs8sKDwzDMkCLDHy2b4LPDYy9ccfXT2uJXgttmeNEmb775r9puaSFno1o2e\nzV4zVCm6nlTwAbOvVHgeBIe1XnKJETbZEyEVSJ7YevYMGhFGjsy+H0AW2AkTjDLBwrDd15y3AkTn\n2KxbR+2SuXy51jy94QYKR7TLwdshbqkUCfb77EOeFElbS5cSnboMfsxnx48P8sVEgniqqziFvOf9\n95vvSpHAxgp9IpHtgZ8xg/gie5cmT6brSI+Gw3BSyXzz5z+n983VpKVAHBcyhBVwC5+8zIQtFA8a\n5M7TtKMcXIWeXJA8+4UXyIvxhz8QfUjPWS5hTaJfv6ACCpjlqLjIzrbbUhtvuSU60sG1sDynOfTq\nRe/X7iPJs3h+sw1SbLiVa9TZmDUryF+6dTM0GlZ1nXOhpVBs31tr41E47zwSdGXut2yj5Ac2//n0\nU6Kdbt3QxHOe7Q3i+eynPzXHqqpM/h/nfMtKyQwWYu+80yx1BFD/ckEROy9SKpDc3h/8wBggHn/c\nXOfxx03+oRwLTLtS5rCRTJLyOWCAWRps7dpoGcQ2/HL7WqNAsvcuzAPJfcF8Lirvt6WF6JrfZXk5\nfW69lZwHYctx8JzHxlrXslVhUQ7/+Q/Jb3xN5m39+4cbqbjf2Ft5112RYzhhe9jkeFi6NKhsPfcc\nXYtTyADiYfKcrl3DIxjWrjV8iyNRXDKZC6mUqbcCkPwEUF8sXUpzpV0TpabG5HVK2AYXzm0F6P3K\nFAM2YtpIJKjtqRStZZwnvAIJELE1NZHlj0MdtSYi2n57YnAyZJQt/4ceagaAzZivu44m4UMPNSGo\nNrjYBU94QDCu2l5zjAVQgFz1m25KzMW2urz5ZjaRck7If/5DW6kA2SGV7D1i4quooP5wlb92JYev\nXEnXOOww+j5qVJD4pbeUYYewSoEXICHqpZdoIrSF0SefpBAotirxBDJgAD1bKkVhLQAlVbNAWFMT\nzC8FDOOyJwkOpeLzbKFGFlZh8MC3LT3c33JhW2mhS6XMsg/HHRdkmr16kWBqM9LVq6lt3C5mTKtX\nxy/y889/mmqEgJlc77+fFvyW1+AQbsm4k0mahJQibzVXOh07lpSExkYSPvfYg4T97t2pGFSUgstC\nkL1cCGCq+V1/fXa+rizkwALPL3+Jj08/nQw606fTpDR3Lq07yiE6AL3nAQPM2I7yQMrQnPJyUi56\n9jTKRVUVVdIFKF+Si5Wccw4dGzGC+pbvK73SNvh/HBbFfW9XYQXISPLb39L+RReRF43HIB8HDG1+\n9VV0efvTT8/OsXEJUvX11D8yl0/ex1Y0UimqSin5A5/nMtLw8/7qV4YPvPQSTb7ffmuE1YceMnTF\nfHbiRLru1Kn04VBql3DHz3bnnUHe3qcP0ZbMUz3kEHruyy4jIfekk4J9yQqWzGMuK8uqLr1egbSF\nz7i5MRJ2CKv9jKmUKYAhc5FTKQrJXLYsW2Czoxz4WaKWC5JKzIQJdC+G1tnv2CWsuRRIgIRLnktS\nKRK8AAq9TqVMv+WKdHApkEyPs2bRuLKFd8mzxo41i4pL8DJIrvuy4DlyZNCINWQIjZ8JE4LznFwu\niI1zUoG0UV9PPK6+3uR5P/54tqL57rvZUQcS//nPegUy3akT8ThbgeR+PvRQ4meDBgWN1lOnEj+0\n5RnAzJuy8jxg+vvqq7PzIl0eSMAUTpFe1iVL3GOBeTobpl9+meaFMWOC4cTffEPGrb32onZ+/DEJ\n+eeeS8fs0GO+Hhupi+GB5HaHKZDs4X7hBdpyP7hSDdjoKPvNfi+usSIVEV6yZexY6g+WE1zP+NJL\nJKu++WaQn+y2G9GVXZGexzPz7PfeM88UMYazFEg5puwIOH5GwNB+RUXQuLjrrrSEViJBtCfHu+RJ\n7FW/5JJw762EdBAB5jn79yfacaXMhfFWXiYIMOuT/+Qn9L1Tp2C6BKeTJRLB8d7YaIpUFrDmvFcg\nARJm6+qok7mjASIUHhwybIMtPb/4hSEs25L1738bxj15snH9JxJmHUa+zoIFZuBw5a3NNjMTImAU\nWxZmli8nIn7rLTMY2FJtK3vXXGOEVM5xDBPOAFJakkkzsbKlmiEtH0pRDpes3PjBBzQYX3+dGFVl\nZVAIY2seg3Mx3nsvyGAk+vUjRaa5OSgQXHopCXAXXGC8Hc89R//hePbZs4OL7fLanPfdR4wjkaAB\n16WLsZRNmEBCz157kXVz4cKgIGP334QJ2dY0OfBd1QLluoZSSAzzVHBRAldxHA5LYebA9HjMMfGL\n/DCN8jWYnk47LXsJGQ4Lu/LK4HUrKuhd1dYGl4dgj2h1NZ3/6ac05n7zGxISLr7Y3T5WwhnyHbgE\nPyBYQZLDBjN98sWhhxL9sXU7maSqgjL/Ztgwc5/LLjNWUIYUoKTCd8EFREe8dA0XweEQYJlDzMUV\nmpuDQoYrzM0GWz9tD2RFhaHLHj2M1/Dll4N9KCdTFuBkFT8Xxo8nJRQgXqK1e+28+noSjlmY5nEn\nvc1S8ZTpAAw+r6UlaFSS4NzjxkYSlNNpijJ45BH6fc0acw/mrUuXmvd9443GSGWH5NXWGkPVbbcF\nJ9zlyw1vZYW+UyeiifPOIyEi7HmSSTKkAHSOVV06VIFk/jpjRrYhJwxcfZC9ArZ3QAoy0qMsPYu2\nwGZHOTzwAH3/9a/DjUDSes5hzRLSiAaQwsbX4jDIN9/M9sRxKPC++xo6sitFRuVzAmRU/MMfgm2S\nua0AKdPffpttwZfKzsKFZHCSRijGvfcSfXERLaYlHm9DhpjQQ4B4xLp1JjyWc3/vv99UiOfxG6VA\n/v732TmIWtN4kkVwZBExIMjPlCIevXixmVt69iSDtTQc8dxVXU0KZHMzvUP2Ym+2GSk5nMMusd9+\nxCP5GuzNXbLERBxIPPggGSf5d6kI8fNIXH21MZ7LIjo8Rpcto+977km099prRlFNp8k7w163qioa\ni0xnLS1khJK0aXsgOa+8EAVy7lyiBVaGworosAIpI1vCUg1kdWaG3YeuscJz7eefG/7Q3EyyK9OM\n6xmfftq0R/KTYcNoXFxwQdB4xevsnncefWevdY5opYAC2bUrGZVlCplMOeNnnDyZDD+jR1O7OLUJ\nIG/94MH0rHa1WKlAsufxxRfDvbcS0kEEmLmnX7/gvCGXr5PrmkvIqMGyMpLzpePlkUeIj0yYQOf2\n7UsyiT1G+N0II70GrJPc8AokYASRtWuD3sBZs0xSrhQyWFOXnkV+mXYI5IsvEjOdM4cG2oQJxmPJ\nxNfcHBTCVq0iC4h0KX/zDb1onjA5hFUSnSvUEKABwJMET67MQFx5YB98QBamd98lQpYTTP/+JkwR\noBywgw9en8vQ7/HHKSzu22/pmTp3JmFWCsa2sMoKX02N6Qd7uYC6umAOBcd4s5LNCcnSurNiBf3P\nFlLkchLHH28E/S22oGe/6CJi3KyIP/IIedxkP0gG3KMHKVv2WpcyNO6f/zSCEVtEpdeRw3fsHMSw\n3FuXcCcnAha8paCdK9eQc2x5QpBKvLxGmPAp+8MWFLnddvhbQwN5ty680K3kjhsXpFG5pI4rrp/B\nYVHjx5uqZf/6V/j5dvv/9z+jILECypAeSDlp9upFSgr/zkVw2Cgln4P5yfvvBz2ChSiQn35K/3v1\nVTOxde1q6J6XNODrs2eAv19zjfFYA9lhvWVlpBCyQF5eTmGBrrXz1q0jIVx6mgCK5OD1TDOJ+pgw\nIXucb7UV8cpkkuiR18YE3EuI8HMDQWH4Rz8ya+RyJdyDDgrSF9O57eF78kljqGpqoudlvt7URP0p\nr8NeB7mmJqNrV/M8gElncIzLClYgmV64fRdfTMVGTjzRhCNGLXOQShll/49/pO1jjwWVTynIyPzr\nceOMIcnOb7eL6Bx0kLuAnITMI1MK+OST4O/2XCCrH3PUyPPPZ4d0Sf7B3l07fHncOBLkhwwx1ToZ\nbCS8+GKT3gBQCJgrrP2DD4I5ujJqh+slTJ5M850c5+k0KTHXXEP3YuWNx+nq1UGDFBeOksIvPwdA\n12YlIapwzt//nn2sqoras/nmwTbKfTbA8PFly8x7AMhY8sgjwVxzHj+ci7dkCXnDpALZs2f2MkSb\nbGKMlLzeLIcCf/EF0bltJBw5ktr01lt0fymvufDOO7T+IGD6mYuJAfReXVVyH3zQzPlz59I7WL06\nuz3pdHD+e+MN2vLcpJRZSidO+g2jttYUX+LomrVrs8N5pQcymQzm+7rmZlZCpaxgrwN+5pk0d0qP\nGsu8rigVHi8uBXLbbWlrR2gxn7zsMjp20kl0T1uu2GMPov2hQ43jxYGAAjloEBmIOYVKpl/wmu1c\nT2LNGprn2WHC466x0UT2XXNNkP9IBVKuucz/i5Kzkkmi6003pbHCyxT17BnUMZqajIGR5Rd7qSpp\nkOLVIzjFqrk5aAhcsoTuIfmxhFJGnujdG8uAz7NPyoZXIAGzjsrNNwdL5be0GO+DywMpFUjO17HX\njHzlFSK8ZJIKuLzzDn1/6qnstclqaogQFi6kSU/mI/CAYwVw+XIStmT4wXPPmTXlJMaMyY7n5gHA\nnhCJl1+mgX3vvXSeDM3ZZx+TOAwQIxfLY2z24otBwmXhs18/I2jYyfTMIKWVipkat7OuzgyOrbcm\nYZT7i2F7Rrfbbn0BAABmGRXbCsWCfjpNbZHhkAytaUJkJiL7uH9/8p6edBJ5LNnjJz238ny7cidA\n1sIZM+jdsEeFF/hm4VNaGO2iO2EKJCNXrmEqZZ6baV0aJJQyVjHOC3JdN5XKVh4Bc74d/iYZoIv5\n8uLrDKmUNzUZK7wr/wgIhp+ecgqtteeCnNRffJGEDVaQbKWAFcTaWrJYsyFg3jwyqCxaFJxwXItQ\n82L3diW2fBXIVIoU43Sa7slCW0WFyRGTivbEicGwY554ZAQFV5Zj8PP36UP9fc89wXEnQxi5iI7L\nWp5M0mTIoX0NDSacfP/9qTrioEHGEPPhh9R2vv9BB2UL901Nhn8zr5HPFrUcBitotgfS9tA0NQWN\nPbbQyEKSC7JiLxBc5gUIjJ9uCxfSu2UhNCwflqtchgmiMoKBt9dfb6I0TjqJjh13HG2feca0MZkk\nhY3DuCVsBZKNEW+9FS4YJ5Mm0qJ792zrt70kxJVXGgWZLeI2b5BKLmBCiidOpD6TPHPLLUlpnT49\nOCbnzDFtkeN7+HAKXWMwvX3yicnR3WsvSlGRUMqENZ94YpBOW1pojI0bZ2QHrjBaVxccnzx38vp/\nHOXEfH+nnSi6Aoj2QNro2pX6c9UqGmdckKu8nEJPub18f05b4f5hA7lczoTfCSvTixZRWgBASx2x\nwti7N83ndi45533Z70JGrsgaAIcdZuQBpgmZNwmYeUAW/eIxwMUJpdz1xhvu+f6II4LeM8mjJeQ8\nnEpRCgc/P9Na9+40V+y5Z/xooDvuMP3F/fHEE9nhvMuWkYzK1/vlL8ML1ABuDyT3Kdf2uPTS7Hz0\nkKWHAJjxPX9+Nh9gw8cvfxkcl2x0Y9q66SaSv12G8549KerKHsMCCTnPs6GRHTDSqcMGK66uWl9v\nzme+wusvswzKkQAXXUQKY3296bNx43IXD2TDAeftf/01jUGOSKqszDZisjGysdE4WC65xKRfzJlD\nsiunynB/MU/o2TO47Nsmm9C5NTXZspJSNCaeeILmn759sRRwVMzMhlcgJZqbg8nwiYQh+JUrjfXn\n7rvppUhBmRngHnsEJw9eBiGVImb15Zf0otkqxuD8xq+/pkH+6adB6xoTKQtrn39OzPygg0x7m5vp\nw8IbM9HPPycPQN++ZhAzA9E6vBplSwsRnaxO9b3vBScCuV9Zia/Gjg0ygb59qe/WrDEJvrYVjxmQ\ntFJxriZPWO+/b8KI2LMii/ooZcJ0gWA+2Lnn0pYHttb0niRDS6VMf4etfQgYIUYy4M6dTQgDKxwN\nDcSIGdddZxjI++8bzy6/o5YWUti5vDOQzVSSSSO4XHwxeYjHjCHP0Pz51MfMXO2Q6ueeCwqytbVk\nlWcr48yZ2c8tBRtmaDfdRDlGO+2UXVyK+yesKiFgwt/YKyQLhMiwDQnp8ZOVgO+5h8LnttvOCJQ8\nWTz8cLZ3o6WF1tpzQSoEUsBwjY2VK6kfvv99egfLltH958935zCw0iAVSGb2zPwZcRTIqipq18cf\n06Qmw5153FRUmHUiJYYNI6FA3nvcuKDB4YQT6He7La+8QpNnKhXMM5LVSBsa6L/S2CGfS6YIpNPG\nKzlyJH14jPOkKWly112pwEwYdtkl6F3g+7qEEsCMEXucuTxq0ttj5/zZ6/sqZZ7XVhjtKoaXX06W\n9x/8AL1feslEbqRS0QtLNze7c5xSKVO0BQgq1DLkd8IEouPq6mDFbsa6daYIEvMIW4EE6P/PPGNy\nomzPaGOj8Tq6ytTb6yuywH/KKUFlQL67ZJK8AhyB86tfkXKxYgXNJZIf8fVtJTRszuvdO2ikOvvs\n7BL5LS1mjWfGrFn07BwifcMN1PecmzRuXFB4Yx4pK5jKNRlZYWFZgo0k1dUm71oqkFEKSTJJ73PQ\nILrvLruYglwvvkiySGVl8P6cesLg+U7O3YkEef/Z2/nOO0FeNH8+jbHKSvecOmAAKZAcdscGoLDC\nZul0tqzAHiUGyzNnnpn9jl1K4Jo1Zk5hI2nfviTnsIOA1++V/JBz97j4XSqVzYuZ1srLaUyycpxr\nSSIgOH/zO7G9XddcQ4bO1183PCOZJDlr8GD63fYkujyQLCe5anU0NNAcu3Zt8Pl5KZREgmTLsjLi\nvVJBTqXMPH/WWcFxyR5ACZadKiqCcoWknfr6YLpUBlkhrPJ5pIdfKuVsFGEF0l5/+YQTguN15kya\nOwGie+7vv/89KMfJwnozZhBfPPdcUvrPP5/Gydq1Ron+5huK6LHBRgCm+T33NOkXXEvBXi+aecJm\nm5lrnn8+He/UySi8to7C8t233wLvvx97HUhorb/zn9HchRUVWtfWar3rrvT95JO1fucd2j/+eO5m\n8+ncmc6vrQ0eO+ssulZZmTln2jT6bl9DKdr26UPn8X2U0rpTJ3Pec8/RdupUra+91hyvrNS6qkrr\nRIL25T342vwZPlyvxx//SMfOOova6GoboPWIEVovWmS+33KL1pdc4j73//5Pz5kzR+suXbTeYw96\nnsMP13r77en3H/+Ytv/6lw7gnnvo+Gmn0X+0zu6v3XfXeu+9aX/oUPpd3rtXL63/9rfgsUQi+L28\n3Oz/7nfBNkyblt1fgNZjx5rjZWWmffX15pyRI9394fooRe3o1o2u1bmzaeuZZ2afP21asJ0rVtDx\nHXZwX5/prbExeHzJEnONuXODz1pVpfVFF2Vfa4893H3Cnz59dBZqa4P9bH+4//73v+zf+H9VVVpP\nn07PPmdO8Jw99wx+339/rQ88MPud33UXXYePZ8biv6+7LrvN3O7Onen/FRW536P9jNXVWu+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aLJIf0jR2anC4XRn+SrLHPFzTOU43n33c13Wxa0FQueV+XHTk2StMM5mpzOJPmtTBmx564pU0wf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\nQmCC\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Prediction error plot is saved to./predictionErrorPlot.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZcYBbuWR03R",
        "colab_type": "text"
      },
      "source": [
        "# Online Recurrent Extreme Learning Machine (OR-ELM)\n",
        "\n",
        "## Base operation: recurrent layer with nonlinear activation using NumPy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YnpxaO-NR7mL",
        "colab_type": "code",
        "outputId": "203a724c-50e7-43a3-e8b8-e03319d6e41c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "def linear_recurrent(features, inputW,hiddenW,hiddenA, bias):\n",
        "  V = np.dot(features, np.transpose(inputW)) + np.dot(hiddenA,hiddenW) + bias\n",
        "  return V\n",
        "\n",
        "def layerNormalization(H, scaleFactor=1, biasFactor=0):\n",
        "\n",
        "  H_normalized = (H-H.mean())/(np.sqrt(H.var() + 0.000001))\n",
        "  H_normalized = scaleFactor*H_normalized+biasFactor\n",
        "\n",
        "  return H_normalized\n",
        "\n",
        "inputW = np.random.random((20,100))\n",
        "print('input_weights:',inputW.shape)\n",
        "\n",
        "hiddenW = np.random.random((20,20))\n",
        "print('hidden_weights:',hiddenW.shape)\n",
        "\n",
        "bias = np.random.random((1,20)) * 2 -1\n",
        "print('bias:', bias.shape)\n",
        "\n",
        "features = np.random.random((1,100))\n",
        "print('input features:', features.shape)\n",
        "\n",
        "hiddenA = np.random.random((1,20))\n",
        "\n",
        "hidden = linear_recurrent(features,inputW,hiddenW,hiddenA,bias)\n",
        "print('hidden:',hidden.shape)\n",
        "\n",
        "hidden = layerNormalization(hidden)\n",
        "print('hidden:',hidden.shape)\n"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('input_weights:', (20, 100))\n",
            "('hidden_weights:', (20, 20))\n",
            "('bias:', (1, 20))\n",
            "('input features:', (1, 100))\n",
            "('hidden:', (1, 20))\n",
            "('hidden:', (1, 20))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g5KJHFrYVhXD",
        "colab_type": "text"
      },
      "source": [
        "## Model hyper-parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tbx98i3jVgWW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "nDimInput = 100\n",
        "nDimOutput = 1\n",
        "numNeurons = 25\n",
        "lamb=0.0001\n",
        "LN=True\n",
        "InWeightFF=1.0\n",
        "OutWeightFF=0.92\n",
        "HiddenWeightFF=1.0\n",
        "AE=True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PGcaA7dJsRTo",
        "colab_type": "text"
      },
      "source": [
        "## ORELM implementation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0058qOaSrOV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ORELM(object):\n",
        "  def __init__(self, inputs, outputs, numHiddenNeurons, LN=True, AE=True,\n",
        "               inputWeightForgettingFactor=1.0,\n",
        "               outputWeightForgettingFactor=1.0,\n",
        "               hiddenWeightForgettingFactor=1.0):\n",
        "    self.name = 'ORELM'\n",
        "    self.inputs = inputs\n",
        "    self.outputs = outputs\n",
        "    self.numHiddenNeurons = numHiddenNeurons\n",
        "\n",
        "    # input to hidden weights\n",
        "    self.inputWeights  = None\n",
        "    # hidden layer to hidden layer wieghts\n",
        "    self.hiddenWeights = None\n",
        "    # initial hidden layer activation\n",
        "    self.initial_H = None\n",
        "    self.H = self.initial_H\n",
        "    self.LN = LN\n",
        "    self.AE = AE\n",
        "    # bias of hidden units\n",
        "    self.bias = None\n",
        "    # hidden to output layer connection\n",
        "    self.beta = None\n",
        "\n",
        "    # auxiliary matrix used for sequential learning\n",
        "    self.M = None\n",
        "    \n",
        "    self.forgettingFactor = outputWeightForgettingFactor\n",
        "\n",
        "\n",
        "    if self.AE:\n",
        "      self.inputAE = OSELM(inputs = inputs,\n",
        "                            outputs = inputs,\n",
        "                            numHiddenNeurons = numHiddenNeurons,\n",
        "                            forgettingFactor=inputWeightForgettingFactor,\n",
        "                            )\n",
        "\n",
        "      self.hiddenAE = OSELM(inputs = numHiddenNeurons,\n",
        "                             outputs = numHiddenNeurons,\n",
        "                             numHiddenNeurons = numHiddenNeurons,\n",
        "                             forgettingFactor=hiddenWeightForgettingFactor,\n",
        "                             )\n",
        "\n",
        "\n",
        "  def __calculateInputWeightsUsingAE(self, features):\n",
        "    self.inputAE.train(features,features)\n",
        "    return self.inputAE.beta\n",
        "\n",
        "  def __calculateHiddenWeightsUsingAE(self, features):\n",
        "    self.hiddenAE.train(features,features)\n",
        "    return self.hiddenAE.beta\n",
        "\n",
        "  def calculateHiddenLayerActivation(self, features):\n",
        "    \"\"\"\n",
        "    Calculate activation level of the hidden layer\n",
        "    :param features feature matrix with dimension (numSamples, numInputs)\n",
        "    :return: activation level (numSamples, numHiddenNeurons)\n",
        "    \"\"\"\n",
        "\n",
        "\n",
        "    if self.AE:\n",
        "      self.inputWeights = self.__calculateInputWeightsUsingAE(features)\n",
        "\n",
        "      self.hiddenWeights = self.__calculateHiddenWeightsUsingAE(self.H)\n",
        "\n",
        "    V = linear_recurrent(features=features,\n",
        "                         inputW=self.inputWeights,\n",
        "                         hiddenW=self.hiddenWeights,\n",
        "                         hiddenA=self.H,\n",
        "                         bias= self.bias)\n",
        "    if self.LN:\n",
        "      V = layerNormalization(V)\n",
        "    self.H = sigmoidActFunc(V)\n",
        "    #self.H = reluActFunc(V)\n",
        "\n",
        "    return self.H\n",
        "\n",
        "\n",
        "  def initializePhase(self, lamb=0.0001):\n",
        "    \"\"\"\n",
        "    Step 1: Initialization phase\n",
        "    :param features feature matrix with dimension (numSamples, numInputs)\n",
        "    :param targets target matrix with dimension (numSamples, numOutputs)\n",
        "    \"\"\"\n",
        "    self.initial_H = np.random.random((1, self.numHiddenNeurons)) * 2 -1\n",
        "    self.H = self.initial_H\n",
        "\n",
        "    self.M = inv(lamb*np.eye(self.numHiddenNeurons))\n",
        "    self.beta = np.zeros([self.numHiddenNeurons,self.outputs])\n",
        "\n",
        "    # randomly initialize the input->hidden connections\n",
        "    self.inputWeights = np.random.random((self.numHiddenNeurons, self.inputs))\n",
        "    self.inputWeights = self.inputWeights * 2 - 1\n",
        "    self.bias = np.random.random((1, self.numHiddenNeurons)) * 2 - 1\n",
        "    \n",
        "    if self.AE:\n",
        "      self.inputAE.initializePhase(lamb=0.00001)\n",
        "      self.hiddenAE.initializePhase(lamb=0.00001)\n",
        "    else:\n",
        "      # randomly initialize the input->hidden connections\n",
        "      self.inputWeights = np.random.random((self.numHiddenNeurons, self.inputs))\n",
        "      self.inputWeights = self.inputWeights * 2 - 1\n",
        "\n",
        "      # hidden layer to hidden layer wieghts\n",
        "      self.hiddenWeights = np.random.random((self.numHiddenNeurons, self.numHiddenNeurons))\n",
        "      self.hiddenWeights = self.hiddenWeights * 2 - 1\n",
        "\n",
        "\n",
        "  def reset(self):\n",
        "    self.H = self.initial_H\n",
        "\n",
        "  def train(self, features, targets):\n",
        "    \"\"\"\n",
        "    Step 2: Sequential learning phase\n",
        "    :param features feature matrix with dimension (numSamples, numInputs)\n",
        "    :param targets target matrix with dimension (numSamples, numOutputs)\n",
        "    \"\"\"\n",
        "    (numSamples, numOutputs) = targets.shape\n",
        "    assert features.shape[0] == targets.shape[0]\n",
        "\n",
        "    H = self.calculateHiddenLayerActivation(features)\n",
        "    Ht = np.transpose(H)\n",
        "    try:\n",
        "      scale = 1/(self.forgettingFactor)\n",
        "      self.M = scale*self.M - np.dot(scale*self.M,\n",
        "                       np.dot(Ht, np.dot(\n",
        "          pinv(np.eye(numSamples) + np.dot(H, np.dot(scale*self.M, Ht))),\n",
        "          np.dot(H, scale*self.M))))\n",
        "\n",
        "      self.beta = self.beta + np.dot(self.M, np.dot(Ht, targets - np.dot(H, self.beta)))\n",
        "\n",
        "\n",
        "    except np.linalg.linalg.LinAlgError:\n",
        "      print \"SVD not converge, ignore the current training cycle\"\n",
        "    # else:\n",
        "    #   raise RuntimeError\n",
        "\n",
        "  def predict(self, features):\n",
        "    \"\"\"\n",
        "    Make prediction with feature matrix\n",
        "    :param features: feature matrix with dimension (numSamples, numInputs)\n",
        "    :return: predictions with dimension (numSamples, numOutputs)\n",
        "    \"\"\"\n",
        "    H = self.calculateHiddenLayerActivation(features)\n",
        "    prediction = np.dot(H, self.beta)\n",
        "    return prediction"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jAettO70Vsj5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net = ORELM(inputs=nDimInput,outputs=nDimOutput, numHiddenNeurons=numNeurons, outputWeightForgettingFactor=0.92)\n",
        "net.initializePhase(lamb=0.0001)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3CxoxEu9hPEf",
        "colab_type": "text"
      },
      "source": [
        "### Online learning and prediction of OR-ELM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wph6x2WlXAr8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "  predictions= []\n",
        "  target= []\n",
        "\n",
        "  for i in xrange(numLags, len(df)-predictionStep-1):\n",
        "    net.train(X[[i], :], T[[i], :])\n",
        "    Y = net.predict(X[[i+1], :])\n",
        "\n",
        "    predictions.append(Y[0][0])\n",
        "    target.append(T[i][0])\n",
        "\n",
        "    print \"{:5}th timeStep -  target: {:8.4f}   |    prediction: {:8.4f} \".format(i, target[-1], predictions[-1])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QXfs-Rt9hEFs",
        "colab_type": "text"
      },
      "source": [
        "### Evaluation: Calculate total Normalized Root Mean Square Error (NRMSE)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AHD6hFzjX-te",
        "colab_type": "code",
        "outputId": "1a9431f8-055c-449f-a401-6a413ae120a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Reconstruct original value\n",
        "predictions = np.array(predictions)\n",
        "target = np.array(target)\n",
        "predictions = predictions * stdSeq + meanSeq\n",
        "target = target * stdSeq + meanSeq\n",
        "  \n",
        "\n",
        "# Calculate NRMSE from skip_eval to the end\n",
        "skip_eval=100\n",
        "squareDeviation = computeSquareDeviation(predictions, target)\n",
        "squareDeviation[:skip_eval] = None\n",
        "nrmse = np.sqrt(np.nanmean(squareDeviation)) / np.nanstd(predictions)\n",
        "print(\"NRMSE {}\".format(nrmse))"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NRMSE 0.259309521862\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j-t2rjibhDiw",
        "colab_type": "text"
      },
      "source": [
        "### Plot predictions and target values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v2arVr2rc9t7",
        "colab_type": "code",
        "outputId": "8051a47b-6a2e-487b-bf0d-26dac293d04c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "source": [
        "  algorithm= net.name\n",
        "  plt.figure(figsize=(15,6))\n",
        "  targetPlot,=plt.plot(target,label='target',color='red',marker='.',linestyle='-')\n",
        "  predictedPlot,=plt.plot(predictions,label='predicted',color='blue',marker='.',linestyle=':')\n",
        "  plt.xlim([5500,6500])\n",
        "  #plt.ylim([0, 30000])\n",
        "  plt.ylabel('value',fontsize=15)\n",
        "  plt.xlabel('time',fontsize=15)\n",
        "  plt.ion()\n",
        "  plt.grid()\n",
        "  plt.legend(handles=[targetPlot, predictedPlot])\n",
        "  plt.title('Time-series Prediction of '+algorithm+' on '+dataSet+' dataset',fontsize=20,fontweight=40)\n",
        "  plot_path = './predictionPlot.png'\n",
        "  plt.savefig(plot_path,plot_pathbbox_inches='tight')\n",
        "  plt.draw()\n",
        "  plt.show()\n",
        "  plt.pause(0)\n",
        "  print('Prediction plot is saved to'+plot_path)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ib0lwHL6zlhfNrIdz7sO6zjd5FnUoG5/Pe865njWWTJxB7ZZhW6hu2eLLcaJz7vlaTCde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K9o0eg6ATdTBy2T6rNksSw+ih1UR66Uat+g/DHWRwp5+rRQVpMOMckyM5hSI+mQUOKbJtMxMN1b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ZQ2uOUYEk70JTJSL0cj8xnBHYcpJZoaIOn8YoXuDH9WdNsy0MT+JtOElTqzEsZ+vXv/FgiU/o\nyXukDkk1fWLlcoFX4ghgIxMHrzIwxEkZ5InAa8XOx4zps48p1scYzVCVuXd8b34pTB44nWrcAljj\nrDj7XKZOstkXOg8CAahUCRzxFkUgFgfOSfcaaqOuQ2qalRYX/0Fz1jOLZDy+ZuBykXhiFzb8KpEa\nzMohuDMaiiEdwom2cKCpoFbSNYtm/upbcjLEx+OhJWPlOTwnGrJh+SnuzZjJ6I23mLbYkBcntuxF\npYdseHEwb1dzPs+4mYtP7Sw2PkYlMizkXmO4j3no/tXFKokFMH8+lCgBZSelQJ8+6pWWFrsxfCF1\ntP/XX/+kB/PgQRG7PT8phxml7W63OrZVC+QXtm/TRtzo+euy+8yMqS3nq4d33zFOEjgr4Av15uWx\nuRgUtiuOn9xG6fYsyi88foF9AbHEm2+GbVS9l9X5Td6gn7oJi1ljQEqKIm4A9dNM8/4XejCj4eef\nVcvWkCHmts+4O78UIjpQ43ZKm9nSvty63N4Us8eI2y1it8tohoRILoI5fuYZXpIz8Ymm9x+lpEhO\nr6giTvKFeh6zxR3XxnT7Qqcw59zl73HM/kui3YbB7VYDpE8f089fJCxbJlKmjEiNGsXSPBFRGvfv\nvScyfHhBG43ynfnJuoLiIFMGtkgN+ZxcwXszHyRut0jnNkdDPi93bIRfcVZfsbjG7oFfSBzZAgGx\n4pUa1j05fsd0Py2KsDCBs3n8dB4fc07/bWGI5b0ZnltrOc8Rv5qvFgM/nRc5MYD1n8ch/NuDWTzw\n+++5ZWvBoCplMyMpGaboD4iFTBz053U8ci2sWkUAC8N5kVG8QKr1ZvTkesYbmAf6c214grcgJCcg\nWMgkgZkkFwsJC6cT4ixB1ROKj7v5DCDnfV7dK7Nw5gyojJoStt9DTfrzJu/S0/Tzdy6cTnDEa1it\n6mcxM+9/Bps3w+TJ0NXkFkJX5fvxY0WwkI2dfqvuY9mJa0K9KQHscSZn70PlYRaCaDkrDGqt4RIO\nUNJ73PTMs9MJ8Q7Bgp9gSF8NNPzEMcvXxXT7ztXcVQj3OFqKZ5m7rsM776hXMVoZDKNkSfWM3rfP\ndHm/qNiwAXr0UNUSZp3Cc0tQs4nj1Q034MeGYCOIhXRLRVOfc7oOX6y8Qa/XDAAAGghJREFUiDUD\n59OC9ZCjOQmgEQyIKZVi58L1Qzn8WFGzFxu7A9UQNLUKXAzmMnqvJjS66AgJZGAh3H4R8jG+Vqb7\nQcitkripvYZFU/3BXs2B69FZxcrP5MQAAeNaaP4NMGOM8EUFRWhilvRR3hIcsLCRa2lLGh5a8iLD\nGcdgnNoq9Lf/Y/6g0HV+qNAu9CZMzgDv0x3PEx+abp+uw8p3tjPa8gKpJJHMbOrzK7cxv1gE6KCu\nt80G55bofHbZ86afv3MRdtDhnsJiZl6xxwsvwNixcO+9kJkJDUzmB3Em1yAh1C+oIfhCE7/wPTjh\n1qXmX+PkZJy2tSGxcTVGLARZw/WcsFQwPQmj6zDhTStWC+QtvQJU/3wxyMIkJ4PNlkueA6pE1oEX\n56N1TLXtfxEuF/h8xk4A/wr69oWePeHll+GK/9fe3UfJVZcJHv8+3U2DghDkJeFVMgqrMHgQQ6DP\ncaAhswI7OgkDcuJyFlQwk+FtV1AICwRckFlEDrILOiAg0XUnIMPbHMGISItOwjuEMWCGlpchuCjy\nHgkJSZ79495KitgdQlKpeyv9/ZxzT9361e3qp6qfvlXPvb/f7360ujhW7YLazXJyRQ4W9/s//b5a\nfJD0jXqcb3IK71kxsRgri7fG+PQK9R++Fd1NhVvjPRzHA7X4LjNnDjz6ys68waYsL/fRXSwt9jFd\nv6jFfhCKVDv33JUHymszAVGTxtCpdg6hscBcz/r7i8IyophsoKr/hz894lxMqPJ1vsLdHMCrbMGE\nnp8zZ8/2Tz40lMP/fH65luVtFLPGPTKqqpDepm/Knpx616d566PjuCRO4WqO45aeI+pRoFOEcPnl\n0NXV+AJYvI+H/9edKo1rOM1jCvXuzJ9fTPATsXJ22Sr19cGdx/4jX+SqcgKnlQeJIHlxzB4VRlfq\n66PvuD24nBPYiLfoYik9LONGDueF6f+7Fon44otFx7WVH9PJxizm6K+MqUV8jX3MRhsFXV3Qu9Fy\n/nb8I9x5xW/om+I46nerii+Aa2rOHLjqKnjmGTjnHHj88epi6esriu+pk55nateVfIvj2aScxK6H\nt7is62T6TvuL6gJs1t9PX++D3MkEzudsrmBKMRcCE+gb81TV0dE3ZU8uP2oO3Syl+bvWXPaCU0+t\nfD8zMABLl3dR7AO7CJIpXFUUvzX5rtVQ9wPllcS3Jv1oR/qyLmMwMzMnTcp83/syqx7eteq4mS7e\netsYgXYNz1uj/vCzZ+cVXX+b45mTG7F45fjQmly/7MEHc8V7BsuzO5bmFacNVh3Wn5g9u8i/8eOL\n6xRq9Tp5DOadd2Z+9auZy5ZVHUlxva3teXbF2OQVY6lrdB3WxsV3Z7NfXsC0/Ajz8sv7DOSSJVUH\nVpg9u7jEYGN/3R1v1XYfc8EFtRpu1LHq+l5ecEExdqtxGe2hLs9Zxb5z9qQL8wKm5Yl8M49kZjFP\nQ92uHdoY+7v//sUXhojii1iN/sizr3g099r66ZVzS3RVf73xzObroze+by3PCz5517t+7zr5c72O\nWMMxmJUXb52wrGuB+cILxTv9V39V/T5l9uzM/r1fXjEBzNsGTm/UnvjW+J+93DHP3v/0vGD8TbUp\nLjMzZ80qPicaO77GhXWr/vtq3XTyB9FZZxUFSR384heZ+23/VG7CG9nNkuxlUU7d/1f1+/9oOgIz\naa8n8zvfqTqgt5s6deV+pqtrWS2+9GnkWXlwuliGujZ7u/eds2cXhVBjIrs/44m1umh8W9X1CEK2\ndhKYVrriiuJzbV0myezkz/U6WtMCs6cNJ0lHvLlziy4vt99eXL+uytPnfX1wwKdHMfBQUnQ7KLpP\n9vQEl11Ws9P6fX1FVzagTmEBPPhg8VHbbPny2s0srxFg+vTi9rzzinEgdfCJT8DFP9yFqUf+ga5X\nX+H4ic8x5f8cUHVYf6qvD266CYAzH4D77y8u5N5Tk0/Go4+GGTOKMXk9dbj+pUakxhCbf/iH4v7S\npdV/1g0MFP0iIIhIJn/sCbhsPV5yoRXK7zR11OhCOTBQdM+uS5hTpsCee9YvLr2zETkGMyIOiYj5\nETEYEdPW9++7775i4H5VF0Bf1cEHQ29vEFFM0jB1anD33cU/stZMY7xMs7qNm9HI8OyzxQLVj79s\nmDOnmGnyX5/bmrkLP8TJNxxQy1kxm912Gxx/POWF5OuhedzMxRfP9cuVKnP00fCe99RnjGh/fzHm\nvKsL6OrmsZ0PZU7tDkV3lrrOhVDXuLR6NTlO2z4R0Q1cDvxHYAFwf0TcmpmPra/f2ShGliypx44Z\nVn6J6u4uPjj8x313GhMNfO978PzzMGaM76Oq8d3vFrfXXguvvw4nnVRpOMDKGTEbGgfW6vz/MXcu\nHHZYfYr0hsZJj4GB16oORSNY3c5w9fUVl307/vjiAP7NNxe9xNbndeMlrbkRV2AC44HBzHwSICJm\nAhOB9VZg1m3H3PjylwmLF8M//VP1MXWiGvd20Qh0663FwY46FJiNSwk0LtFUlwNrq/PBDxbXIpQ0\ntLp95r34YlFcNnTCgSxppBiJBeYOwLNN9xcA+67vX1qnHXN/f3HJlEWLivvbbFNpOJLWwfTpxf/y\njTf+6bjgqjSf4YfOOLu//fZw991F9966xyrp7b3DoDMOZEkjRWRdvpG0SUQcARySmceV9/8LsG9m\nnrjKdlOAKQCjR4/++MyZM9se6/r0z/+8HZdeuivLlwe9vcu5+OK57LFHe7pgLVy4kM0226wtv0t6\nNzoxN7/5zV1ZvLiL00+f/84ba0jz5m3OySfvxfLlwcYbt3d/uKY6MTc1clSVn/Pmbc6sWaMBOPjg\n39Xu/1bVc9/ZWgceeOCDmTnunbYbiWcwnwOarza/Y9n2Npl5JXAlwLhx47J/Azss1phwIxOWLu3m\ntdf2btuRv4GBATa091Mbhk7MzUa455yzHR/5CEyeXGk4HWnOnJXj0tu9P1xTnZibGjmqys/+fjjh\nhMa9Hdr++1V/7jurMRILzPuBXSNiLEVhORn4z9WG1H51nHhI0tqbMQO22w4+8AG7eL5b7g8lSWqd\nEVdgZubSiDgRmAV0A9dk5ryKw2q7uk08JGntTJ8O8+bB738PCxbAhAnVXmu3E7k/lCSpdUZcgQmQ\nmbcBt1UdR9XqNPGQpLWzaBE8/XRx9m3ZMmdSXFvuDyVJao2uqgOQJK29iy6CSy8t1ru67OIpSZKq\nZYEpSR1un31g883h0EPtHitJkqo1IrvIStKG4gtfgLlz4Uc/srCUJEnV8wymJHWoOXPg+9+Hhx4q\nJvdpXH5IkiSpKhaYktShBgaKa9kCvPkmXH11peFIkiRZYEpSp2pcv7G7u7i//faVhiNJkuQYTEnq\nVH19cPTRcPfdxdlLx2BKkqSqWWBKUgfbbTd4/XWLS0mSVA92kZWkDnbKKfCNb8BRR8Hs2VVHI0mS\nRjoLTEnqcG+8AffdBy+9VHUkkiRppLOLrCR1sEsvheuvhyeeqDoSSZIkz2BKUkfbfHMYM6bqKCRJ\nkgoWmJLUwT7/efj7v4cjjoCHH646GkmSNNJZYEpSh3vjDXj8cfjjH6uORJIkjXSOwZSkDnbVVXDJ\nJcXZy97eqqORJEkjnWcwJamDbbUV7L47ZFYdiSRJkgWmJHW0ww6DadPgyCNh/vyqo5EkSSOdBaYk\ndbhFi+Df/x3eeqvqSCRJ0kjnGExJ6mDXXQdnnQW//CWMHl11NJIkaaTzDKYkdbBttoF99oEeDxdK\nkqQasMCUpA520EHwxS/CUUfBggVVRyNJkkY6C0xJ6nBLlsDLL8Py5VVHIkmSRjo7VUlSB/vxj2HK\nFJg1C3beuepoJEnSSOcZTEnqYNtsAxMmwKabVh2JJEmSBaYkdbSPfxwmToTPfa7oJitJklQlC0xJ\n6nDLl8PSpdDlHl2SJFXMMZiS1MH+5V/g+OPhxhthiy2qjkaSJI10Hu+WpA62zTYwaRJstVXVkUiS\nJFlgSlJH22036OuDY48tuslKkiRVyQJTkjpcTw9ssoljMCVJUvVq83UkIj4TEfMiYnlEjFvlsTMi\nYjAi5kfEwU3th5RtgxExral9bETcW7ZfFxG9ZfvG5f3B8vFd2vX6JGl9mDcPTjoJTj7ZAlOSJFWv\nTl9HfgX8DXB3c2NE7A5MBvYADgG+FRHdEdENXA4cCuwOfLbcFuBC4JLM/BDwMnBs2X4s8HLZfkm5\nnSR1rPe/H446CnbeuepIJEmSalRgZubjmTl/iIcmAjMzc3FmPgUMAuPLZTAzn8zMJcBMYGJEBHAQ\ncEP58zOASU3PNaNcvwGYUG4vSR1pu+3ggx8szmBKkiRVrTYF5mrsADzbdH9B2TZc+1bAK5m5dJX2\ntz1X+fir5faS1LE22wy23bbqKCRJktp8HcyI+CkwZoiHzszMW9oZyzuJiCnAFIDRo0czMDBQbUAb\nkIULF/p+qpY6MTdfeKGXk07al5NPfoKBgeerDkfrSSfmpkYO81N1ZW5Wo60FZmb+5Vr82HPATk33\ndyzbGKb9RWBURPSUZymbt28814KI6AG2KLcfKtYrgSsBxo0bl/39/WsRuoYyMDCA76fqqBNz87XX\n4IQT4LDDPsx++3246nC0nnRibmrkMD9VV+ZmNTqhi+ytwORyBtixwK7AfcD9wK7ljLG9FBMB3ZqZ\nCdwFHFH+/DHALU3PdUy5fgTws3J7SepIm28O730vTJ9edSSSJEk1KjAj4rCIWAD0AT+KiFkAmTkP\nuB54DPgxcEJmLivPTp4IzAIeB64vtwU4HTglIgYpxlheXbZfDWxVtp8CrLi0iSR1qm23hV12qToK\nSZKkNneRXZ3MvAm4aZjHvgZ8bYj224Dbhmh/kmKW2VXb3wQ+s87BSlJNLFoEp5wC559fdSSSJEk1\nOoMpSXr3envhy1+GffetOhJJkiQLTEnqaN3dxUQ/F19cdSSSJEk16iIrSVo7Y8fCJptUHYUkSZJn\nMCWp4519NnS5N5ckSTXgGUxJ6nBnnw3jxlUdhSRJkmcwJanjPfooXHVV1VFIkiR5BlOSOt6HPwyZ\nVUchSZLkGUxJ6njf+Q4sWFB1FJIkSZ7BlKSOd8YZxUyykiRJVbPAlKQOd+21RRfZLbeEvr6qo5Ek\nSSOZXWQlqYPNmQOPPAIPPQQTJhT3JUmSqmKBKUkdbGCgOHuZCUuWFPclSZKqYoEpSR2svx96e6G7\nu7jt7686IkmSNJI5BlOSOlhfH9x5Z3Hmsr/fMZiSJKlaFpiS1OH6+iwsJUlSPdhFVpIkSZLUEhaY\nkiRJkqSWsMCUJEmSJLWEBaYkSZIkqSUsMCVJkiRJLWGBKUmSJElqCQtMSZIkSVJLWGBKkiRJklrC\nAlOSJEmS1BIWmJIkSZKklrDAlCRJkiS1hAWmJEmSJKklLDAlSZIkSS1hgSlJkiRJagkLTEmSJElS\nS9SmwIyIiyLi1xHxaETcFBGjmh47IyIGI2J+RBzc1H5I2TYYEdOa2sdGxL1l+3UR0Vu2b1zeHywf\n36Wdr1GSJEmSNmS1KTCBO4A/z8yPAv8GnAEQEbsDk4E9gEOAb0VEd0R0A5cDhwK7A58ttwW4ELgk\nMz8EvAwcW7YfC7xctl9SbidJkiRJaoHaFJiZ+ZPMXFrevQfYsVyfCMzMzMWZ+RQwCIwvl8HMfDIz\nlwAzgYkREcBBwA3lz88AJjU914xy/QZgQrm9JEmSJGkd1abAXMUXgNvL9R2AZ5seW1C2Dde+FfBK\nU7HaaH/bc5WPv1puL0mSJElaRz3t/GUR8VNgzBAPnZmZt5TbnAksBX7QzthWFRFTgCkAo0ePZmBg\noMpwNigLFy70/VQtmZuqK3NTdWZ+qq7MzWq0tcDMzL9c3eMR8TngU8CEzMyy+Tlgp6bNdizbGKb9\nRWBURPSUZymbt28814KI6AG2KLcfKtYrgSsBxo0bl/39/WvwCrUmBgYG8P1UHZmbqitzU3Vmfqqu\nzM1q1KaLbEQcApwG/HVmvtH00K3A5HIG2LHArsB9wP3AruWMsb0UEwHdWhamdwFHlD9/DHBL03Md\nU64fAfysqZCVJEmSJK2Dtp7BfAeXARsDd5Tz7tyTmVMzc15EXA88RtF19oTMXAYQEScCs4Bu4JrM\nnFc+1+nAzIg4H3gYuLpsvxr4fkQMAi9RFKWSJEmSpBaoTYFZXjpkuMe+BnxtiPbbgNuGaH+SYpbZ\nVdvfBD6zbpFKkiRJkoZSmy6ykiRJkqTOZoEpSZIkSWoJC0xJkiRJUktYYEqSJEmSWsICU5IkSZLU\nEhaYkiRJkqSWsMCUJEmSJLWEBaYkSZIkqSUsMCVJkiRJLWGBKUmSJElqCQtMSZIkSVJLWGBKkiRJ\nklrCAlOSJEmS1BIWmJIkSZKklrDAlCRJkiS1hAWmJEmSJKklLDAlSZIkSS1hgSlJkiRJaonIzKpj\nqL2IeAF4puo4NiBbA3+oOghpCOam6srcVJ2Zn6orc7O1PpCZ27zTRhaYaruIeCAzx1Udh7Qqc1N1\nZW6qzsxP1ZW5WQ27yEqSJEmSWsICU5IkSZLUEhaYqsKVVQcgDcPcVF2Zm6oz81N1ZW5WwDGYkiRJ\nkqSW8AymJEmSJKklLDDVEhHxdET8a0Q8EhEPNLWfFBG/joh5EfH1pvYzImIwIuZHxMFN7YeUbYMR\nMa3dr0MbnqFyMyL2ioh7Gm0RMb5sj4j4X2X+PRoRezc9zzER8US5HFPV69GGJSJGRcQN5X7y8Yjo\ni4j3R8QdZa7dERFbltuan2qbYXLzovL+oxFxU0SMatrez3W1xVC52fTYqRGREbF1ed/9ZhUy08Vl\nnRfgaWDrVdoOBH4KbFze37a83R2YC2wMjAV+A3SXy2+APwN6y212r/q1uXT2Mkxu/gQ4tFz/T8BA\n0/rtQAD7AfeW7e8HnixvtyzXt6z6tbl0/gLMAI4r13uBUcDXgWll2zTgwnLd/HRp2zJMbn4S6Cnb\nLmzKTT/XXdq2DJWb5fpOwCyKa9dvXba536xg8Qym1qe/A/5nZi4GyMzfl+0TgZmZuTgznwIGgfHl\nMpiZT2bmEmBmua3UaglsXq5vAfy2XJ8IfC8L9wCjImI74GDgjsx8KTNfBu4ADml30NqwRMQWwP7A\n1QCZuSQzX6HIwxnlZjOASeW6+am2GC43M/Mnmbm03OweYMdy3c91tcVq9psAlwCnUXzGN7jfrIAF\nplolgZ9ExIMRMaVs2w34i4i4NyJ+HhH7lO07AM82/eyCsm24dmldDJWb/w24KCKeBb4BnFG2m5tq\np7HAC8B3I+LhiLgqIjYFRmfm/yu3eR4YXa6bn2qX4XKz2RcozgyBuan2GTI3I2Ii8Fxmzl1le3Oz\nAhaYapVPZObewKHACRGxP9BD0fVgP+ArwPURERXGqJFpqNz8O+BLmbkT8CXKI6FSm/UAewPfzsyP\nAX+k6BK7QmYmbz8aL7XDanMzIs4ElgI/qCY8jWBD5ea5wH8HplcYl5pYYKolMvO58vb3wE0U3WIW\nADeW3RLuA5YDWwPPUfSTb9ixbBuuXVprw+TmMcCN5SY/LNvA3FR7LQAWZOa95f0bKL44/a7swkV5\n2xheYH6qXYbLTSLic8CngKPKAyBgbqp9hsvNscDciHiaIs8eiogxmJuVsMDUOiu7JryvsU4xCcCv\ngJspJvohInajGIj9B+BWYHJEbBwRY4FdgfuA+4FdI2JsRPQCk8ttpbWymtz8LXBAudlBwBPl+q3A\n0eWsc/sBr5ZdFWcBn4yILcsZPT9ZtklrLTOfB56NiP9QNk0AHqPIw8aMhscAt5Tr5qfaYrjcjIhD\nKMa4/XVmvtH0I36uqy2Gyc2HMnPbzNwlM3ehKEL3Lrd1v1mBnqoD0AZhNHBT2fu1B/i/mfnj8sPk\nmoj4FbAEOKY82jkvIq6n+CK1FDghM5cBRMSJFP/g3cA1mTmv/S9HG5DhcnMhcGlE9ABvAo2xmbdR\nzDg3CLwBfB4gM1+KiPMoviwB/I/MfKl9L0MbsJOAH5T7yycpcq6LYkjBsRSzIR5Zbmt+qp2Gys37\nKWaKvaPcr96TmVMz0891tdNQuTkc95sViJW9GyRJkiRJWnt2kZUkSZIktYQFpiRJkiSpJSwwJUmS\nJEktYYEpSZIkSWoJC0xJkiRJUktYYEqS1AYRcWR5kfrmtoGIuKGikCRJajkvUyJJUhuUheTWmdnf\n1LY78FZmPlFZYJIktVBP1QFIkjRSZeZjVccgSVIr2UVWkqT1LCKuBQ4HDoiILJdzV+0iW7b9ISL2\njYgHImJRRPwyIsZGxLYRcXNELIyIxyPioCF+z3ERMS8iFkfEMxFxWhtfpiRJFpiSJLXBecBdwMNA\nX7lcNcy27wWuBC4BPgvsDHwf+Efgl8DfAM8BP4yI9zZ+KCK+AnwbuBn4VLl+XkScuB5ejyRJQ7KL\nrCRJ61lm/iYiXgK6MvOeRntEDLX5e4CTM/Pn5TbbA5cD52TmN8q2BcA84ADg9ojYHDgHOD8zv1o+\nzx1lAXpWRHw7M5etp5cnSdIKnsGUJKlelgC/aLo/WN7+bIi2HcrbPmBTirOaPY2l/JnRwI7rMV5J\nklbwDKYkSfXyemYub7q/pLx9pdGQmUvKs5+blE1bl7fzhnnOnYBnWhmkJElDscCUJKnzvVTefgr4\n3RCPz29jLJKkEcwCU5Kk9ljCyjOOrTYHWARsn5k/Wk+/Q5Kkd2SBKUlSe/wamBgRk4AFwG9b9cSZ\n+UpEnAtcGhEfAO6mmGdhN+DAzDysVb9LkqTVscCUJKk9vgV8DLgG2BL46uo3f3cy8+sR8VvgS8Cp\nwJvAvwHXtfL3SJK0OpGZVccgSZIkSdoAeJkSSZIkSVJLWGBKkiRJklrCAlOSJEmS1BIWmJIkSZKk\nlrDAlCRJkiS1hAWmJEmSJKklLDAlSZIkSS1hgSlJkiRJagkLTEmSJElSS/x/j4/uRSVVxdcAAAAA\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Prediction plot is saved to./predictionPlot.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-frWp2nSJKj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "c559cfd8-5c1f-45dc-dcd0-c56751c3c100"
      },
      "source": [
        "  algorithm= net.name\n",
        "  plt.figure(figsize=(15,6))\n",
        "  targetPlot,=plt.plot(np.abs(target-predictions),label='target',color='red',marker='.',linestyle='-')\n",
        "  plt.xlim([5500,6500])\n",
        "  #plt.ylim([0, 30000])\n",
        "  plt.ylabel('absolute error',fontsize=15)\n",
        "  plt.xlabel('time',fontsize=15)\n",
        "  plt.ion()\n",
        "  plt.grid()\n",
        "  plt.legend(handles=[targetPlot, predictedPlot])\n",
        "  plt.title('Prediction error of '+algorithm+' on '+dataSet+' dataset',fontsize=20,fontweight=40)\n",
        "  plot_path = './predictionErrorPlot.png'\n",
        "  plt.savefig(plot_path,plot_pathbbox_inches='tight')\n",
        "  plt.draw()\n",
        "  plt.show()\n",
        "  plt.pause(0)\n",
        "  print('Prediction error plot is saved to'+plot_path)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ADTfckFtvvZVDDjmE6dOnc+aZZ3LLLbdw2mmncfTRRwNw7LHH8uEPf5g77riD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aLJIf0jR2anC4XRn+SrLHPFzTOU43n33c13Wxa0FQueV+XHTk2StMM5mpzOJPmtTBmx564pU0wf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\nQmCC\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Prediction error plot is saved to./predictionErrorPlot.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6mcHzU_ov78b",
        "colab_type": "text"
      },
      "source": [
        "# Exercise: Try with a different time-series\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Io2qT5HHtePU",
        "colab_type": "text"
      },
      "source": [
        "## Dataset\n",
        "* **Bike Sharing Demand** from [kaggle](https://www.kaggle.com/c/bike-sharing-demand/overview) \n",
        " * Historical bike usage patterns are aggregated at 1 hour intervals \n",
        " * You are asked to combine the historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C.\n",
        " * Goal: NRMSE < 0.6 when prediction_step = 1\n",
        " \n",
        " ![bike_image](https://storage.googleapis.com/kaggle-competitions/kaggle/3948/media/bikes.png)\n",
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eBKnKkJeAiZJ",
        "colab_type": "text"
      },
      "source": [
        "### Download the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTrogEW98bkB",
        "colab_type": "code",
        "outputId": "c66c8fce-27f4-4e9d-ba1a-bf6e85cb665e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "!git clone https://github.com/chickenbestlover/sk_hynix_class.git\n",
        "!ls\n",
        "df = pd.read_csv('sk_hynix_class/bike-sharing-demand/train.csv')\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'sk_hynix_class' already exists and is not an empty directory.\n",
            "algorithms\t Online-Recurrent-Extreme-Learning-Machine  README.md\n",
            "data\t\t plot.py\t\t\t\t    result\n",
            "errorMetrics.py  plotResults.py\t\t\t\t    run.py\n",
            "expsuite\t prediction\t\t\t\t    sk_hynix_class\n",
            "fig\t\t predictionPlot.png\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>datetime</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01 00:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-01 01:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>32</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-01 02:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-01 03:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-01 04:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              datetime  season  holiday  ...  casual  registered  count\n",
              "0  2011-01-01 00:00:00       1        0  ...       3          13     16\n",
              "1  2011-01-01 01:00:00       1        0  ...       8          32     40\n",
              "2  2011-01-01 02:00:00       1        0  ...       5          27     32\n",
              "3  2011-01-01 03:00:00       1        0  ...       3          10     13\n",
              "4  2011-01-01 04:00:00       1        0  ...       0           1      1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3HYiY2c8osv",
        "colab_type": "text"
      },
      "source": [
        "## Data Fields\n",
        "\n",
        "**datetime** - hourly date + timestamp\n",
        "\n",
        "**season** -  1 = spring, 2 = summer, 3 = fall, 4 = winter \n",
        "\n",
        "**holiday** - whether the day is considered a holiday\n",
        "\n",
        "**workingday** - whether the day is neither a weekend nor holiday\n",
        "\n",
        "**weather** - \n",
        "\n",
        "  1: Clear, Few clouds, Partly cloudy, Partly cloudy \n",
        "\n",
        "  2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist \n",
        "\n",
        "  3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds \n",
        "\n",
        "  4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog \n",
        "\n",
        "**temp** - temperature in Celsius\n",
        "\n",
        "**atemp** - \"feels like\" temperature in Celsius\n",
        "\n",
        "**humidity** - relative humidity\n",
        "\n",
        "**windspeed** - wind speed\n",
        "\n",
        "**casual** - number of non-registered user rentals initiated\n",
        "\n",
        "**registered** - number of registered user rentals initiated\n",
        "\n",
        "**count** - number of total rentals"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T9cuunlmI67q",
        "colab_type": "text"
      },
      "source": [
        "## Preprocess"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3rl4q5d8x0b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# seperating season as per values. this is bcoz this will enhance features.\n",
        "season=pd.get_dummies(df['season'],prefix='season')\n",
        "df=pd.concat([df,season],axis=1)\n",
        "# same for weather. this is bcoz this will enhance features.\n",
        "weather=pd.get_dummies(df['weather'],prefix='weather')\n",
        "df=pd.concat([df,weather],axis=1)\n",
        "# now can drop weather and season.\n",
        "df.drop(['season','weather'],inplace=True,axis=1)\n",
        "# now most importantly split the date and time as the time of day is expected to effect the no of bikes.\n",
        "df[\"hour\"] = [t.hour for t in pd.DatetimeIndex(df.datetime)]\n",
        "df[\"day\"] = [t.dayofweek for t in pd.DatetimeIndex(df.datetime)]\n",
        "df[\"month\"] = [t.month for t in pd.DatetimeIndex(df.datetime)]\n",
        "df['year'] = [t.year for t in pd.DatetimeIndex(df.datetime)]\n",
        "df['year'] = df['year'].map({2011:0, 2012:1})\n",
        "# now can drop datetime column.\n",
        "df.drop('datetime',axis=1,inplace=True)\n",
        "df.drop(['casual','registered'],axis=1,inplace=True)\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I9xd2CfOCaAy",
        "colab_type": "code",
        "outputId": "8c53e39d-bb86-40da-a91d-214ad686254a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "import seaborn as sns\n",
        "# just to visualize.\n",
        "sns.boxplot(data=df[['temp',\n",
        "       'atemp', 'humidity', 'windspeed', 'count']])\n",
        "fig=plt.gcf()\n",
        "fig.set_size_inches(10,10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AQGcCDACgMwEGANCZAAMA6EyAAQB0JsAAADoTYAAAnQkwAIDOBBgAQGcCDACgMwEGANCZ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\nD7CzjMsqX/5dAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0iYZQRJ1TLAY",
        "colab_type": "text"
      },
      "source": [
        "### Now it's your turn!\n",
        "\n",
        "Start by normalizing the dataset.\n",
        "\n",
        "Then make input-target pairs. Targets should be the 'count' column in the dataset.\n",
        "\n",
        "Declare a predictive network and use it to perform online learning and prediction.\n",
        "\n",
        "Finally, evaluate the performance of your network and visualize forecast results."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-l0nNVnYONQ2",
        "colab_type": "text"
      },
      "source": [
        "### What's more?\n",
        "* Use multi-step prediction model\n",
        "\n",
        "![multistep](https://raw.githubusercontent.com/chickenbestlover/RNN-Time-series-Anomaly-Detection/master/fig/arch.png)\n",
        "\n",
        "* Design anomaly score\n",
        "\n",
        "![anomalyscore](https://raw.githubusercontent.com/chickenbestlover/RNN-Time-series-Anomaly-Detection/master/fig/equation1.gif)\n",
        "\n",
        "* Evaluate anomaly detection performance\n",
        "    * precision, recall, F1\n",
        "    \n",
        "![f1](https://raw.githubusercontent.com/chickenbestlover/RNN-Time-series-Anomaly-Detection/master/fig/fig_f_beta_channel0.png)\n",
        "\n",
        "* Use reconstruction error based anomaly detection model (next lecture)\n",
        "\n",
        "\n",
        "\n",
        "## References\n",
        "* [Malhotra, Pankaj, et al. \"Long short term memory networks for anomaly detection in time series.\" Proceedings. Presses universitaires de Louvain, 2015.](https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-56.pdf)\n",
        "\n",
        "\n",
        "* [Malhotra, Pankaj, et al. \"LSTM-based encoder-decoder for multi-sensor anomaly detection.\" arXiv preprint arXiv:1607.00148 (2016).](https://arxiv.org/pdf/1607.00148.pdf)\n",
        "\n",
        "* [multi-step prediction based anomaly detection tutorial](https://github.com/chickenbestlover/RNN-Time-series-Anomaly-Detection)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0hlYO9YPKZZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}